US dataswitch to UK
Data Scientists
generating standard or custom reports summarizing business, evaluating processes and technologies and collecting business intelligence data from available industry reports. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: maintaining or updating business intelligence tools. The tasks, though, are not you.
It would be a lie to soften that, and another lie to promise a rebuilt version of this job inside this job.
So the hope here is what you already carry: the judgment you bring to the work of data management project staff is real, and the moves below are built from it. The first step is down this page.
Your week, as this page understands it
Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports. The job title says “data scientists”. The real job is the part underneath: supervising the work of data management project staff. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But data scientists is not one task. It is 54 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is supervising the work of data management project staff, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 84%
- changing shape
- 7%
- staying human
- 9%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 75 out of 100 (70–81 allowing for uncertainty): high exposure, across 54 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for data scientists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 10 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
45 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Generating standard or custom reports summarizing business
This is reading one thing and writing another: standard in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Generate standard or custom reports summarizing business, financial, or economic data for review by executives, managers, clients, and other stakeholders.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Standard and custom reports are generated straight from business data, ready for a manager to review.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing and validating clinical databases
This is reading one thing and writing another: clinical databases in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Design and validate clinical databases, including designing or testing logic checks.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Database design and logic checks follow documented standards, with a qualified person approving them in a regulated study.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Processing clinical data, including receipt, entry, verification or filing of information
This is reading one thing and writing another: clinical data, including receipt, entry in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Process clinical data, including receipt, entry, verification, or filing of information.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Receiving, entering, verifying and filing study data is rule-driven processing software performs consistently.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Maintaining or updating business intelligence tools
This is reading one thing and writing another: business intelligence tools in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Maintain or update business intelligence tools, databases, dashboards, systems, or methods.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping dashboards, databases and reporting tools up to date is routine technical maintenance software handles.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
4 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Performing quality control audits to ensure accuracy
The software now makes the first pass at quality control audits, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Perform quality control audits to ensure accuracy, completeness, or proper usage of clinical systems and data.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Automated checks find most data problems, but a qualified person must own the audit findings in a regulated setting.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Training staff on technical procedures or software program usage
The software now makes the first pass at staff, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Train staff on technical procedures or software program usage.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Training material is easy to produce, but staff learn software and procedures best from someone who answers their questions.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Contributing to the compilation, organization and production of protocols, clinical study reports, regulatory submissions or other controlled documentation
The software now makes the first pass at the compilation, organization and production of protocols, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Contribute to the compilation, organization, and production of protocols, clinical study reports, regulatory submissions, or other controlled documentation.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Regulatory documents follow strict, documented formats software can draft, but qualified people must own what is submitted.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
5 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Conferring with end users to define or implement clinical system requirements
The value here is that a specific person handles end users and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Confer with end users to define or implement clinical system requirements such as data release formats, delivery schedules, and testing protocols.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Working out what end users actually need is a back-and-forth conversation where unstated assumptions surface.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Communicating with customers, competitors, suppliers, professional organizations or others to stay abreast of industry or business trends
The value here is that a specific person handles customers, competitors, suppliers, professional organizations and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Communicate with customers, competitors, suppliers, professional organizations, or others to stay abreast of industry or business trends.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (22–30 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Keeping up through conversations with customers, suppliers and peers depends on relationships and things people only say informally.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Supervising the work of data management project staff
The value here is that a specific person handles the work of data management project staff and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Supervise the work of data management project staff.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (14–22 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting the best from a project team depends on knowing individuals and relationships built over time.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Show the other 44 tasks
Analyzing, manipulating or processing large sets of data using statistical software
shifting to AIThis is reading one thing and writing another: large sets of data in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Analyze, manipulate, or process large sets of data using statistical software.” (O*NET task statement)
How this row was scored
Exposure score: 100 out of 100 (96–100 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Processing and analysing large datasets with statistical tools is heavily documented work that software now performs end to end.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Writing new functions or applications in programming languages to conduct analyses
shifting to AIThis is reading one thing and writing another: new functions in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Write new functions or applications in programming languages to conduct analyses.” (O*NET task statement)
How this row was scored
Exposure score: 100 out of 100 (96–100 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing analysis code is one of the strongest and best-documented capabilities software has.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Maintaining library of model documents
shifting to AIThis is reading one thing and writing another: library of model documents in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Maintain library of model documents, templates, or other reusable knowledge assets.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Maintaining a library of templates and reusable documents is routine organising work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying or monitoring current and potential customers
shifting to AIThis is reading one thing and writing another: current and potential customers in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Identify or monitor current and potential customers, using business intelligence tools.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Identifying and tracking customers through business intelligence tools is query and reporting work software performs automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating or reviewing technical design documentation to ensure the accurate development of reporting solutions
shifting to AIThis is reading one thing and writing another: technical design documentation in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Create or review technical design documentation to ensure the accurate development of reporting solutions.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing and reviewing technical design documents is structured, well-documented work software produces to a good standard.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Documenting specifications for business intelligence or information technology reports
shifting to AIThis is reading one thing and writing another: specifications in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Document specifications for business intelligence or information technology reports, dashboards, or other outputs.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing specifications for reports and dashboards follows standard formats that software produces accurately.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating business intelligence tools or systems
shifting to AIThis is reading one thing and writing another: business intelligence tools in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Create business intelligence tools or systems, including design of related databases, spreadsheets, or outputs.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building standard reporting tools, databases and spreadsheets is well-documented technical work software now does end to end.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collecting business intelligence data from available industry reports
shifting to AIThis is reading one thing and writing another: business intelligence data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Collect business intelligence data from available industry reports, public information, field reports, or purchased sources.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Gathering business intelligence from reports and published sources is collection work software does quickly and thoroughly.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Synthesizing current business intelligence or trend data to support recommendations for action
shifting to AIThis is reading one thing and writing another: current business intelligence in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Synthesize current business intelligence or trend data to support recommendations for action.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling together current data into a clear recommendation is synthesis work software does well from available figures.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing appropriate formatting to data sets
shifting to AIThis is reading one thing and writing another: this work in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare appropriate formatting to data sets as requested.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reformatting datasets to a requested specification is mechanical work software performs quickly and accurately.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Tracking the flow of work forms
shifting to AIThis is reading one thing and writing another: the flow of work forms in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Track the flow of work forms, including in-house data flow or electronic forms transfer.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking forms and data as they move through a process is workflow record keeping software handles automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing data analysis listings and activity
shifting to AIThis is reading one thing and writing another: data analysis listings in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare data analysis listings and activity, performance, or progress reports.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing data listings and progress reports from study systems is routine reporting software generates.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying feature selection algorithms to models predicting outcomes of interest
shifting to AIThis is reading one thing and writing another: feature selection algorithms in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Feature selection runs on published algorithms that modelling tools already apply automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Cleaning and manipulating raw data using statistical software
shifting to AIThis is reading one thing and writing another: raw data in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Clean and manipulate raw data using statistical software.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cleaning and reshaping raw data is repetitive, rule-driven work that software carries out reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Comparing models using statistical performance metrics
shifting to AIThis is reading one thing and writing another: models in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing models on standard metrics is a calculation software runs automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating graphs, charts or other visualizations to convey the results of data analysis using specialized software
shifting to AIThis is reading one thing and writing another: graphs, charts or other visualizations in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing charts and visualisations from analysis results is well-documented work software does quickly and to a high standard.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Testing, validating and reformulate models to ensure accurate prediction of outcomes of interest
shifting to AIThis is reading one thing and writing another: reformulate models in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Testing, validating and retuning models is systematic, repeatable work software performs thoroughly.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying and analyzing industry or geographic trends with business strategy implications
shifting to AIThis is reading one thing and writing another: industry in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Identify and analyze industry or geographic trends with business strategy implications.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Industry and regional trend data is abundant and public, so software produces solid analysis for someone to interpret.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Analyzing technology trends to identify markets for future product development or to improve sales of existing products
shifting to AIThis is reading one thing and writing another: technology trends in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Analyze technology trends to identify markets for future product development or to improve sales of existing products.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Technology trend information is abundant and public, so software analyses it well, though market judgement stays with people.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Analyzing competitive market strategies through analysis of related product
shifting to AIThis is reading one thing and writing another: competitive market strategies in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze competitive market strategies through analysis of related product, market, or share trends.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Market and share data is widely published, so software analyses competitors thoroughly, though reading intent takes judgement.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Applying sampling techniques to determine groups to be surveyed or use complete enumeration methods
shifting to AIThis is reading one thing and writing another: techniques in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Sampling methods are textbook statistics software applies accurately.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Designing surveys, opinion polls or other instruments to collect data
shifting to AIThis is reading one thing and writing another: surveys, opinion polls or other instruments in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Design surveys, opinion polls, or other instruments to collect data.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Survey design principles are well documented, though good questions depend on knowing the subject and the people answering.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Reading scientific articles, conference papers or other sources of research to identify emerging analytic trends and technologies
shifting to AIThis is reading one thing and writing another: scientific articles in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Research literature is public and plentiful, so software summarises it well, though judging what genuinely matters takes experience.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Writing work instruction manuals, data capture guidelines or standard operating procedures
shifting to AIThis is reading one thing and writing another: work instruction manuals in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write work instruction manuals, data capture guidelines, or standard operating procedures.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Manuals, guidelines and standard operating procedures are structured documents software drafts well, ready for approval.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Generating data queries
shifting to AIThis is reading one thing and writing another: data queries in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Generate data queries, based on validation checks or errors and omissions identified during data entry, to resolve identified problems.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Generating queries from validation errors is automatic rule checking, with the study team resolving what comes back.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing forms for receiving, processing or tracking data
shifting to AIThis is reading one thing and writing another: forms in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Design forms for receiving, processing, or tracking data.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Data collection forms follow documented standards, so software drafts them, with sign-off needed on a regulated study.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Disseminating information regarding tools, reports or metadata enhancements
shifting to AIThis is reading one thing and writing another: information regarding tools, reports or metadata enhancements in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Disseminate information regarding tools, reports, or metadata enhancements.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Announcing tool and report changes to users is routine internal communication software drafts and sends.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Developing technical specifications for data management programming and communicating needs to information technology staff
shifting to AIThis is reading one thing and writing another: technical specifications in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop technical specifications for data management programming and communicate needs to information technology staff.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Technical specifications follow standard formats software produces accurately, with a person handling the handover conversation.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Developing or selecting specific software programs for various research scenarios
shifting to AIThis is reading one thing and writing another: specific software programs in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Develop or select specific software programs for various research scenarios.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing and choosing software follows documented criteria, though the right fit depends on this organisation's setup and constraints.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying relationships and trends or any factors that could affect the results of research
shifting to AIThis is reading one thing and writing another: relationships in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Identify relationships and trends or any factors that could affect the results of research.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Finding relationships and confounding factors in data is standard statistical work software performs well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reading technical literature and participating in continuing education or professional associations to maintain awareness of current database technology and best practices
shifting to AIThis is reading one thing and writing another: technical literature in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Technical literature is plentiful and software summarises it well, though professional networks also work through people.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Analyzing clinical data using appropriate statistical tools
shifting to AIThis is reading one thing and writing another: clinical data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze clinical data using appropriate statistical tools.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: The statistics themselves are well-defined, but clinical analyses are expected to carry a qualified statistician's name.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Evaluating processes and technologies and suggest revisions to increase productivity and efficiency
shifting to AIThis is reading one thing and writing another: processes in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Evaluate processes and technologies, and suggest revisions to increase productivity and efficiency.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Suggestions for better processes are easy to draft, though what will actually work depends on how this workplace really runs.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Monitoring work productivity or quality to ensure compliance with standard operating procedures
shifting to AIThis is reading one thing and writing another: work productivity in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Monitor work productivity or quality to ensure compliance with standard operating procedures.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking work against written procedures is systematic monitoring software does well, with a responsible person confirming findings.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing project-specific data management plans that address areas
shifting to AIThis is reading one thing and writing another: project-specific data management plans in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop project-specific data management plans that address areas such as coding, reporting, or transfer of data, database locks, and work flow processes.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Data management plans follow well-established templates, though a qualified person approves the plan for a regulated study.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Proposing solutions in engineering, the sciences and other fields using mathematical theories and techniques
shifting to AIThis is reading one thing and writing another: solutions in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Mathematical modelling and proposed solutions can be drafted well, though a qualified specialist signs off technical recommendations.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing technical support for existing reports
shifting to AIThis is reading one thing and writing another: technical support in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Provide technical support for existing reports, dashboards, or other tools.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Most report and dashboard problems are common and documented, so software resolves them, with tricky cases passed on.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Managing timely flow of business intelligence information to users
shifting to AIThis is reading one thing and writing another: flow of business intelligence information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Manage timely flow of business intelligence information to users.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Getting the right information to the right users on time is reporting work software manages well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conducting or coordinating tests to ensure that intelligence is consistent with defined needs
shifting to AIThis is reading one thing and writing another: tests in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Conduct or coordinate tests to ensure that intelligence is consistent with defined needs.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Test plans and checks against stated requirements are documented work software performs, with users confirming the result fits.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Providing support and information to functional areas
shifting to AIThis is reading one thing and writing another: support in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Provide support and information to functional areas such as marketing, clinical monitoring, and medical affairs.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Answering questions and providing information to other departments suits software well, with people handling unusual requests.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Identifying solutions to business problems
shifting to AIThis is reading one thing and writing another: solutions in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning analysis into practical options for budgeting or staffing suits software well, with people choosing among them.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Recommending data-driven solutions to key stakeholders
changing shapeThe software now makes the first pass at data-driven solutions, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Recommend data-driven solutions to key stakeholders.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The recommendation can be written well, but getting senior people to act on it depends on the person making the case.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Delivering oral or written presentations of the results of mathematical modeling and data analysis to management or other end users
staying humanThe value here is that a specific person handles oral and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Slides and written summaries are easy to produce, but presenting to managers and handling their questions is done by a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Identifying business problems or management objectives that can be addressed through data analysis
staying humanThe value here is that a specific person handles business problems and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Identify business problems or management objectives that can be addressed through data analysis.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (31–39 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Spotting which business problems are worth analysing depends on knowing the organisation and talking to the people running it.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $120,230a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 262,440in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: business intelligence tools in, a record out. The rows above are exactly that shape: maintaining or updating business intelligence tools and generating standard or custom reports summarizing business. What it cannot do is be trusted in person, which is what the work of data management project staff runs on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: maintaining or updating business intelligence tools is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 84% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 9% in rows it is nowhere near. That is the position, measured across 54 scored tasks. It is not a forecast about you.
What you have that the software does not is supervising the work of data management project staff, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of business intelligence tools you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of business intelligence tools, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is maintaining or updating business intelligence tools” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of supervising the work of data management project staff you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to data scientists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was mathematical science occupations, all other: only about 2% of its durable work is work you already do, it is under the same pressure this job is, it pays 32.2% less and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 84% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “confer with end users to define or implement clinical system requirements” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Mathematical Science Occupations, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already develop technical specifications for data management programming and communicate needs to information technology…, and their equivalent is to confer with researchers, clinicians, or information technology staff to determine data needs and…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 85% of its own task list already scores in the top exposure band (78/100 in this release), so the same software is eating it. It is a pay cut, in those words: $81,490 against your $120,230, 32.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 3,720 of those jobs against 262,440 of yours (OEWS May 2025), 1% as many seats.
Life, Physical, and Social Science Technicians, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean and manipulate raw data using statistical software, and their equivalent is to monitor raw data quality during collection, and make equipment corrections as necessary. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $62,280 against your $120,230, 48.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Computer Systems Analysts
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already train staff on technical procedures or software program usage, and their equivalent is to train staff and users to work with computer systems and programs. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $105,850 against your $120,230, 12.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 84% of its task weight, across 54 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: supervising the work of data management project staff is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Project support officers is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Project support officers and Data analysts. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for data scientists, and we are not going to point you at the nearest one and call it a fit.
The working behind that
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 84% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for data scientists. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for data scientists launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Data Scientists?
- Not as a job, but it is already doing parts of the work. Across the 54 official task statements scored for Data Scientists (United States, SOC 15-2051), 84% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 75 out of 100 (range 70–81, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Data Scientists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Analyze, manipulate, or process large sets of data using statistical software” (100/100, very high); “Write new functions or applications in programming languages to conduct analyses” (100/100, very high); “Maintain library of model documents, templates, or other reusable knowledge assets” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Data Scientists” stay human?
- About 9% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Supervise the work of data management project staff” (18/100, minimal); “Communicate with customers, competitors, suppliers, professional organizations, or others to stay abreast of industry or business trends” (26/100, low); “Identify business problems or management objectives that can be addressed through data analysis” (35/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Data Scientists” do about AI?
- Start from the ledger rather than the headline: 84% of this job's weighted core work is exposed, and roughly 9% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Data Scientists calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 54 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 10 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt-with-imputed)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.