US dataswitch to UK
Social Science Research Assistants
designing and creating special programs, performing descriptive and multivariate statistical analyses of data and tracking research participants and performing any necessary follow-up tasks. 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: verifying the accuracy and validity of data entered in databases. The tasks, though, are not you.
It would be a lie to soften that; obtaining informed consent of research subjects or their guardians is what this work rebuilds around. The routes below start from it.
Your week, as this page understands it
Assist social scientists in laboratory, survey, and other social science research. May help prepare findings for publication and assist in laboratory analysis, quality control, or data management. The job title says “social science research assistants”. The real job is the part underneath: obtaining informed consent of research subjects or their guardians. 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 social science research assistants is not one task. It is 22 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is obtaining informed consent of research subjects or their guardians, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 72%
- changing shape
- 4%
- staying human
- 24%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 63 out of 100 (57–69 allowing for uncertainty): high exposure, across 22 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 social science research assistants 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.
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.
- 2 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
14 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.
Verifying the accuracy and validity of data entered in databases
This is reading one thing and writing another: the accuracy in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Verify the accuracy and validity of data entered in databases, correcting any errors.” (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: Checking entries against rules and fixing errors is routine automated validation.
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.
Providing assistance with the preparation of project-related reports
This is reading one thing and writing another: assistance in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Provide assistance with the preparation of project-related reports, manuscripts, and presentations.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: Drafting and formatting reports, manuscripts and slides from existing results is exactly the sort of writing software does 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.
Performing data entry and other clerical work as required for project completion
This is reading one thing and writing another: data entry in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform data entry and other clerical work as required for project completion.” (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: Data entry and routine clerical work on digital records is one of the easiest things to automate.
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 tables, graphs, fact sheets and written reports summarizing research results
This is reading one thing and writing another: tables, graphs in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare tables, graphs, fact sheets, and written reports summarizing research results.” (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: Turning results into tables, charts and summary text is largely mechanical, though deciding what matters 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 3/4.
Designing and creating special programs
This is reading one thing and writing another: special programs in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Design and create special programs for tasks such as statistical analysis and data entry and cleaning.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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 and data-cleaning scripts is well-documented programming that AI drafts strongly, with a researcher checking the logic.
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.
Changing shape
1 taskTasks 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.
Recruiting and scheduling research participants
The software now makes the first pass at research participants, 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 4 · SupplementalSource: “Recruit and schedule research participants.” (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: Scheduling is easily automated, but persuading people to take part still leans on human contact.
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.
Staying human
7 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.
Administering standardized tests to research subjects
This work happens in the physical world: standardized tests, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Administer standardized tests to research subjects, or interview them to collect research data.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Test administration and interviewing usually happen face to face on a set protocol, so a person runs the session.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Obtaining informed consent of research subjects or their guardians
This work happens in the physical world: informed consent of research subjects, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Obtain informed consent of research subjects or their guardians.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Consent forms are easy to draft, but rules require a trained person to explain the study and answer questions.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Presenting research findings to groups of people
This work happens in the physical world: research findings, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Present research findings to groups of people.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Slides can be drafted by software, but standing in front of a room and answering questions is a person job.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Screening potential subjects to determine their suitability as study participants
The rules require a named, qualified person to answer for potential subjects, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Screen potential subjects to determine their suitability as study participants.” (O*NET task statement)
How this row was scored
Exposure score: 36 out of 100 (29–43 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; someone qualified has to answer for it.
The rating behind it: Screening records against entry criteria is structured checking, though a clinician confirms who is suitable.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Show the other 12 tasks
Preparing, manipulating and managing extensive databases
shifting to AIThis is reading one thing and writing another: extensive databases in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare, manipulate, and manage extensive databases.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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 and maintaining large databases is standard, heavily documented technical work that AI assists with strongly.
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.
Coding data in preparation for computer entry
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Code data in preparation for computer entry.” (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: Assigning codes to responses is a well-documented, repeatable job software does well, though open-ended answers need checking.
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.
Performing descriptive and multivariate statistical analyses of data
shifting to AIThis is reading one thing and writing another: descriptive in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform descriptive and multivariate statistical analyses of data, using computer software.” (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: Running descriptive and multivariate statistics in software is well-documented work AI does well, though choosing the right model 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 3/4.
Conducting internet-based and library research
shifting to AIThis is reading one thing and writing another: internet-based in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Conduct internet-based and library 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: Searching literature and online sources is something AI does quickly and broadly, with occasional trips to physical archives.
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 4/4.
Providing assistance in the design of survey instruments
shifting to AIThis is reading one thing and writing another: assistance in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Provide assistance in the design of survey instruments such as questionnaires.” (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: Questionnaire design is well documented, so software drafts solid instruments that a researcher then refines.
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.
Tracking laboratory supplies and expenses
shifting to AIThis is reading one thing and writing another: laboratory supplies in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Track laboratory supplies and expenses such as participant reimbursement.” (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: Tracking supplies and reimbursements is routine bookkeeping, with occasional stock checks in the lab.
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.
Editing and submitting protocols and other required research documentation
shifting to AIThis is reading one thing and writing another: protocols in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Edit and submit protocols and other required research documentation.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Editing and submitting research protocols is document work, though the lead researcher still signs them off.
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 and implementing research quality control procedures
shifting to AIThis is reading one thing and writing another: research quality control procedures in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop and implement research quality control procedures.” (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: Quality-control procedures are well documented and easy to draft, but getting a team to follow them needs a person.
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.
Tracking research participants and performing any necessary follow-up tasks
shifting to AIThis is reading one thing and writing another: research participants in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Track research participants, and perform any necessary follow-up tasks.” (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: Tracking participants and sending follow-ups is scheduling and reminder work that software manages reliably.
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.
Performing needs assessments or consulting with clients to determine the types of research and information
staying humanThe value here is that a specific person handles needs assessments and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Perform needs assessments or consult with clients to determine the types of research and information required.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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 a client actually needs comes from conversation and reading between the lines.
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.
Allocating and managing laboratory space and resources
staying humanThis work happens in the physical world: laboratory space, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Allocate and manage laboratory space and resources.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Allocating bench space and lab resources depends on knowing the physical room and who is in it.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Supervising the work of survey interviewers
staying humanThe value here is that a specific person handles the work of survey interviewers and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Supervise the work of survey interviewers.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Supervising interviewers depends on watching how they work and coaching them, which software cannot do.
The five ratings: output a model can produce 1/4 · 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.
What this job pays, and how many people do it
- Median pay
- $61,990a 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
- 30,640in 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: the accuracy in, a record out. The rows above are exactly that shape: verifying the accuracy and validity of data entered in databases and providing assistance with the preparation of project-related reports. What it cannot do is be there in the room, and that is still where informed consent of research subjects gets done. 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: verifying the accuracy and validity of data entered in databases is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 72% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 24% in rows it is nowhere near. That is the position, measured across 22 scored tasks. It is not a forecast about you.
What you have that the software does not is obtaining informed consent of research subjects or their guardians, 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 the accuracy 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 the accuracy, 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 verifying the accuracy and validity of data entered in databases” 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 obtaining informed consent of research subjects or their guardians 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 social science research assistants (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was natural sciences managers: only about 4% of its durable work is work you already do and the 2.7× pay gap is the market pricing a barrier. I am not going to pretend that is comfortable news: 72% 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. “administer standardized tests to research subjects, or interview them to collect research…” 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.
Natural Sciences Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already obtain informed consent of research subjects or their guardians, and their equivalent is to oversee subject enrollment to ensure that informed consent is properly obtained and documented. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $167,220 against your $61,990 is 2.70× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Sociologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present research findings to groups of people, and their equivalent is to present research findings at professional meetings. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 2,260 of those jobs against 30,640 of yours (OEWS May 2025), 7% as many seats.
Historians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present research findings to groups of people, and their equivalent is to conduct historical research, and publish or present findings and theories. 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. And it is a narrow door: about 3,450 of those jobs against 30,640 of yours (OEWS May 2025), 11% as many seats.
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: 72% of its task weight, across 22 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: obtaining informed consent of research subjects or their guardians 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 social science research assistants, 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 72% 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 social science research assistants. 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 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 Social Science Research Assistants?
- Not as a job, but it is already doing parts of the work. Across the 22 official task statements scored for Social Science Research Assistants (United States, SOC 19-4061), 72% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 57–69, 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 “Social Science Research Assistants” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Verify the accuracy and validity of data entered in databases, correcting any errors” (93/100, very high); “Perform data entry and other clerical work as required for project completion” (93/100, very high); “Prepare, manipulate, and manage extensive databases” (83/100, 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 “Social Science Research Assistants” stay human?
- About 24% 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: “Obtain informed consent of research subjects or their guardians” (14/100, minimal); “Supervise the work of survey interviewers” (17/100, minimal); “Administer standardized tests to research subjects, or interview them to collect research data” (18/100, minimal). 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 “Social Science Research Assistants” do about AI?
- Start from the ledger rather than the headline: 72% of this job's weighted core work is exposed, and roughly 24% 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 Social Science Research Assistants 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 22 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.
- 2 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.
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.