◆ Education

What an education policy analyst
really does.

20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.

20evidenced tasks
29,030in the US (2025)
$105,650median pay / year
6systems it runs on
See the 20 tasks ↓Open in the atlasThe full career map →
This is what one task looks like here
Collect data
Pull statewide assessment, enrollment, and attendance extracts for 201…3 sources agree

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The work, task by task

20 tasks
Hands on the work16
Produce statistical reports and visualizations+
Generate quarterly statistical summaries and create district comparison charts showing proficiency rates, chronic absenteeism, and subgroup gaps, then export a PDF briefing with figure captions for Friday's board packet.
jdonetwiki3 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Apply statistical methods to data+
Clean and merge the district enrolment and assessment datasets, run descriptive summaries and regression models to estimate program effects, produce tables of coefficients with 95% confidence intervals, and prepare charts for the policy brief.
escojdonetwiki4 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Analyze data to identify trends+
Aggregate three years of school performance and labor-market placement data, create trend lines and cohort comparisons, identify significant shifts by subgroup, and draft a one-page summary of implications for the next funding proposal.
jdonetwiki3 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Identify statistical patterns+
Identify patterns in statewide assessment results by cleaning the score dataset, computing subgroup means and growth rates, testing for significant differences across districts and socioeconomic groups, and flagging anomalies for follow-up within two working days.
esco
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Gather data+
Assemble the last five years of enrollment, attendance, and assessment records from district extracts, reconcile mismatched student IDs, create a tidy merged dataset with consistent variable names, and produce a data quality report by Wednesday.
esco
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Process data+
Clean and join the district enrollment, attendance and finance tables, standardise school and LEA codes, flag missing entries, then produce a single cleaned dataset ready for analysis by Friday so we can start cohort tracking next week.
esco
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Think analytically+
Test whether the 2018 policy change plausibly affected graduation rates: outline rival explanations, list required variables and assumptions, and produce a short decision memo recommending the best identification strategy by Wednesday.
esco
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Perform data analysis+
Run descriptive and subgroup summaries on reading scores by year, grade and free-lunch status, produce tables of means and counts with confidence intervals, and export the outputs for the policy team to review on Monday.
esco
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Watch and assess3
Grow the practice1

What the work runs on

named inside the evidenced tasks
9 tasksIBM SPSS Statisticsexecutes statistical tests and creates publication-quality charts for reporting performance and subgroup differences
6 tasksMicrosoft Excelformats tables and calculates summary metrics for insertion into the briefingOpen its task library →
4 tasksMicrosoft Officecreates the decision memo and supporting documentation summarising analytic choices for stakeholders
1 taskIBM DB2stores and serves district-level relational data for extracting student and assessment records
1 taskMicrosoft Accessjoins multiple administrative tables and enforces field types to produce a single analysis-ready table
1 taskLinuxprovides the environment and scripting to orchestrate reproducible pipelines and run statistical scripts reliably
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The same task, four heights

this page is height one
ExecuteDo today's task, with fewer mistakesyou are here → ImproveMake it easy for the next person to acceptin the atlas → DecideWork out the right move when it is unclearin the atlas → BecomeLearn the pattern so it stops coming backin the atlas →

Can AI actually do this job?

the honest answer

It can

where it genuinely helps
  • Explain the theory behind the work
  • Draft, tidy and structure your writing
  • Rehearse a hard conversation before you have it
  • Build a study plan that fits your gaps

It cannot

where it stops, completely
  • Be in the room where an education policy analyst actually works
  • Carry the responsibility when the call is wrong — that weight stays yours
  • Notice what no one wrote down: the hesitation, the thing left unsaid
  • Live with the outcome

What the work pays

two countries, two different measures

United States

this exact occupation · BLS 2025
  • $105,650 a year — the middle: half earn more, half earn less
  • The lowest tenth earn near $64,000; the top tenth near $174,050
  • 29,030 people employed in this occupation

India

the occupation GROUP, not this job · PLFS via ILOSTAT 2025
  • ₹26,152 a month — the median for Technicians and associate professionals, the group this work sits in
  • India publishes pay by broad occupation group, so this covers many jobs besides this one. It is a shape, not a salary.
read this carefullyThese two numbers are not comparable and must not be converted into each other. One is a yearly figure for this job alone; the other is a monthly figure for a whole family of jobs. What travels between them is the pattern, not the amount: experience lifts pay almost everywhere.

Where the evidence lives

open any of it yourself
O*NETThe US Department of Labor's own record of this job6 of these tasks ESCOThe European skills and occupations register11 of these tasks Job descriptionsReal postings, in the wild11 of these tasks WikipediaHow the world describes this work7 of these tasks US Bureau of Labor StatisticsPay and employment, by occupation2025 figures UK NOS SFJPDPS2.7.3Analyse, benchmark and present policy evaluation datanational standard UK NOS COGPOLY24Assess data and produce reportsnational standard

Close to this work

12 nearby
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Questions people actually ask

You spend much of the day with data: collecting state or district datasets, cleaning them in Excel or SQL, and running tests in R, Python, or SPSS. Expect meetings with program staff or managers in the morning to define the questions and the data needed.

Afternoons often mean building charts or tables for reports, writing the methods and findings, and checking sources. Some days are fieldwork—requesting data from districts or reviewing administrative rules—so the schedule can jump between coding and writing.

Start with Excel and SQL. Excel handles quick cleaning and charts; SQL (used with IBM DB2 or other databases) pulls and joins large administrative files. These two let you work with real education data immediately.

Next learn R or Python for statistical tests, models, and reproducible scripts. SPSS is OK if your office already uses IBM SPSS Statistics, but R/Python give more flexibility for visualization and advanced analysis.

Use AI for drafting report language, summarizing methods, or generating code snippets, but never for final answers. Always verify any code or statistical result AI provides by running it yourself and checking assumptions, tests, and outputs.

Never paste identifiable student data into public AI tools. Treat AI outputs as a first draft: check facts, cite original sources, and document every analytic step for reproducibility.

You will apply statistical principles daily: descriptive statistics, trend analysis, regressions, and hypothesis tests like t-tests or chi-square for group differences. You also build models to estimate program effects or predict enrollment.

Tasks include assessing data reliability, running diagnostics for model fit, and producing visualizations that show trends over time. Software choices are R, Python, SAS, or SPSS depending on your office.

According to the U.S. Bureau of Labor Statistics (BLS), this occupation (SOC 15-2041.00) had 29,030 employed and a median annual wage of $105,650. The lowest 10% earned about $64,000 and the top 10% about $174,050, per BLS 2025 data.

Use those ranges to set expectations: local government or nonprofits often sit near the median, while federal roles or specialized research centers can reach the top end.

Compared with a data scientist: you focus specifically on education policy questions, program evaluation, data quality, and policy writing. You use statistics and models, but less often machine learning production pipelines or big data engineering.

Compared with a teacher: you rarely deliver instruction or manage classrooms. You analyze administrative and survey data to inform decisions that affect schools, rather than directly teaching students.

Learn to collect and clean data, run basic statistical analyses, and make clear charts. Practically: practice SQL queries on linked student files, clean messy spreadsheets in Excel, and run regressions in R or SPSS.

Also practice writing two-page briefs and creating one-page visuals that explain findings to managers. Employers look for demonstrated analytic work and the ability to explain methods and limitations clearly.