Will AI replace Mathematical Science Occupations, All Other?

Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Mathematical Science Occupations, All Other (United States, SOC 15-2099), 85% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 78 out of 100 (range 73–84, 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 “Mathematical Science Occupations, All Other” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases” (100/100, very high); “Write computer programs or scripts to be used in querying databases” (100/100, very high); “Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation” (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.

What should someone working in “Mathematical Science Occupations, All Other” do about AI?

Start from the ledger rather than the headline: 85% of this job's weighted core work is exposed, and roughly 15% 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 Mathematical Science Occupations, All Other 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 19 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.

US dataswitch to UK

Mathematical Science Occupations, All Other

analyzing or manipulating bioinformatics data using software packages, writing computer programs or scripts to be used in querying databases and documenting all database changes, modifications or problems. 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: analyzing or manipulating bioinformatics data using software packages. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 85%
changing shape: 0%
staying human: 15%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 78 out of 100 (73–84 allowing for uncertainty): high exposure, across 19 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 mathematical science occupations, all other 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 2026-08-05. Read the full method.

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 researchers is real, and the moves below are built from it. The first step is down this page.

Your week, as this page understands it

All mathematical scientists not listed separately. The job title says “mathematical science occupations” or “all other”: officially one job, two names. The real job is the part underneath: conferring with researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities. 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 mathematical science occupations, all other is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 85%
changing shape: 0%
staying human: 15%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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 2026-08-05. 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.

1 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

16 tasks

Tasks 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.

Analyzing or manipulating bioinformatics data using software packages
This is reading one thing and writing another: bioinformatics data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques.” (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: Analyzing biological datasets with statistical and data mining tools is screen work with well documented methods.
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.
Conducting quality analyses of data inputs and resulting analyses or predictions
This is reading one thing and writing another: quality analyses of data inputs in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Conduct quality analyses of data inputs and resulting analyses or predictions.” (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: Checking whether inputs and predictions are sound needs domain judgment, though the checks themselves are automated easily.
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.
Participating in the preparation of reports or scientific publications
This is reading one thing and writing another: the preparation of reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Participate in the preparation of reports or scientific publications.” (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: Drafting sections of a report or paper is writing that tools do well, with the science team steering.
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.
Maintaining awareness of new and emerging computational methods and technologies
This is reading one thing and writing another: awareness of new and emerging computational methods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain awareness of new and emerging computational methods 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: Keeping up with new computational methods is reading and summarizing published work, which tools do quickly and broadly.
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.
Documenting all database changes, modifications or problems
This is reading one thing and writing another: all database changes, modifications or problems in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Document all database changes, modifications, or problems.” (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: Recording database changes and problems is documentation that can be generated automatically from the systems themselves.
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 data management or error-checking procedures and user manuals
This is reading one thing and writing another: data management in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Create data management or error-checking procedures and user manuals.” (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 procedures and user manuals for data handling is exactly the kind of technical writing tools produce well.
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.
Developing or maintaining applications that process biologically based data into searchable databases for purposes of analysis
This is reading one thing and writing another: applications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation.” (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: Building applications that turn biological data into searchable databases is software development, where tools now perform strongly.
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

0 tasks

Tasks 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

3 tasks

Tasks 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 researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities
The value here is that a specific person handles researchers and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities.” (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 value is that a specific person does it.
The rating behind it: Working out what researchers actually need takes back and forth conversation before anything can be built.
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.
Conferring with database users about project timelines and changes
The value here is that a specific person handles database users and stands behind it. That is earned, not computed.
importance 4 · Supplemental
Source: “Confer with database users about project timelines and changes.” (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 value is that a specific person does it.
The rating behind it: Agreeing timelines and changes with users depends on reading their priorities and negotiating, which needs a person.
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.
Training bioinformatics staff or researchers in the use of databases
The value here is that a specific person handles bioinformatics staff and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Train bioinformatics staff or researchers in the use of databases.” (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: Training researchers to use a database is done live, adapting to what each person is struggling with.
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.

Show the other 9 tasks

Entering or retrieving information from structural databases
shifting to AI
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases.” (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: Querying public sequence, structure and expression databases is documented, repeatable work that tools do very well.
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 computer programs or scripts to be used in querying databases
shifting to AI
This is reading one thing and writing another: computer programs in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Write computer programs or scripts to be used in querying databases.” (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 query scripts against a documented database is the clearest example of work these tools do at expert level.
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.
Designing or implementing web-based tools for querying large-scale biological databases
shifting to AI
This is reading one thing and writing another: web-based tools in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Design or implement web-based tools for querying large-scale biological databases.” (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: Building a web interface over a biological database is standard development work that tools do to a usable 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.
Extending existing software programs, web-based interactive tools or database queries as sequence management and analysis needs evolve
shifting to AI
This is reading one thing and writing another: software programs in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Extend existing software programs, web-based interactive tools, or database queries as sequence management and analysis needs evolve.” (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: Extending existing programs and queries is coding, which tools now do to a standard developers accept with light editing.
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.
Packaging bioinformatics data for submission to public repositories
shifting to AI
This is reading one thing and writing another: bioinformatics data in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Package bioinformatics data for submission to public repositories.” (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: Formatting data to meet a public repository’s published specification is a documented conversion job.
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.
Developing, or applying dataing mining and machine learning algorithms
shifting to AI
This is reading one thing and writing another: machine learning algorithms in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Develop or apply data mining and machine learning algorithms.” (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: Applying known machine learning methods is well supported by tools, though genuinely new algorithm work still needs a researcher.
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.
Testing new or updated software or tools and providing feedback to developers
shifting to AI
This is reading one thing and writing another: new or updated software or tools in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Test new or updated software or tools and provide feedback to developers.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 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: Testing software and writing up what broke is well structured work that automated testing already covers substantially.
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.
Monitoring database performance and performing any necessary maintenance
shifting to AI
This is reading one thing and writing another: database performance in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Monitor database performance and perform any necessary maintenance, upgrades, or repairs.” (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: Watching database performance and applying upgrades is scriptable screen work with abundant published guidance.
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.
Performing routine system administrative functions
shifting to AI
This is reading one thing and writing another: routine system administrative functions in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Perform routine system administrative functions, such as troubleshooting, back-ups, or upgrades.” (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; mistakes that are cheap to catch.
The rating behind it: Backups, upgrades and troubleshooting are largely scripted already, though some hardware still needs hands on it.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $81,490a 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: 3,720in 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: bioinformatics data in, a record out. The rows above are exactly that shape: analyzing or manipulating bioinformatics data using software packages and conducting quality analyses of data inputs and resulting analyses or predictions. What it cannot do is be trusted in person, which is what researchers run 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: analyzing or manipulating bioinformatics data using software packages is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 85% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 15% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.

What you have that the software does not is conferring with researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities, 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 bioinformatics data 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 bioinformatics data, 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 analyzing or manipulating bioinformatics data using software packages” 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 conferring with researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities 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 mathematical science occupations, all other (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was biological scientists, all other: only about 2% of its durable work is work you already do. I am not going to pretend that is comfortable news: 85% 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 researchers, clinicians, or information technology staff to determine data needs…” 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.

Biological Scientists, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already enter or retrieve information from structural databases, protein sequence motif databases, mutation databases…, and their equivalent is to plan or conduct basic genomic and biological research related to areas. 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.
Look at that job’s page anyway →
Computer Network Architects
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already monitor database performance and perform any necessary maintenance, upgrades, or repairs, and their equivalent is to coordinate network operations, maintenance, repairs, or upgrades. 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.
Look at that job’s page anyway →
Mining and Geological Engineers, Including Mining Safety Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already develop or apply data mining and machine learning algorithms, and their equivalent is to evaluate data to develop new mining products, equipment, or processes. 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.
Look at that job’s page anyway →

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: 85% of its task weight, across 19 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: conferring with researchers, clinicians or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities 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:

Data analysts

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

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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for mathematical science occupations / all other, and we are not going to point you at the nearest one and call it a fit.

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 85% of the work on this page is already inside what they can do.

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The AI Authority is a general community about working with AI, not a course for mathematical science occupations / all other. 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.

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No Space for mathematical science occupations / all other yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for mathematical science occupations / all other existed, with researched problems, courses and people in the same boat, would you want in?

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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 Mathematical Science Occupations, All Other?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Mathematical Science Occupations, All Other (United States, SOC 15-2099), 85% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 78 out of 100 (range 73–84, 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 “Mathematical Science Occupations, All Other” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases” (100/100, very high); “Write computer programs or scripts to be used in querying databases” (100/100, very high); “Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation” (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 “Mathematical Science Occupations, All Other” stay human?: About 15% 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: “Confer with database users about project timelines and changes” (35/100, low); “Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with data…” (35/100, low); “Train bioinformatics staff or researchers in the use of databases” (39/100, 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 “Mathematical Science Occupations, All Other” do about AI?: Start from the ledger rather than the headline: 85% of this job's weighted core work is exposed, and roughly 15% 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 Mathematical Science Occupations, All Other 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 19 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.
1 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)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
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.

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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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.