Data Scientist Resume Template

Data scientist resume template for Word and Google Docs. Experience leads, and each bullet asks for the baseline, the model, how it shipped and what it moved.

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What is in the file

[Your Name]

[Data Scientist] · [Focus: experimentation, forecasting, NLP, recommendations or risk] · [Python and SQL]

[City, State] · [email@example.com] · [(555) 555-0100] · [LinkedIn or portfolio URL]

Summary

[Data scientist with N years in (industry) working on (problem type). Best shipped work: (model or study), which (changed what metric or decision, measured how). Strong in (two methods or tools from the posting).]

Experience

[Data Scientist], [Company], [Team or product area]

[City, State or Remote] · [Mon YYYY] – [Present]

[Built (model type) to (predict or rank what) for (users or team), beating (baseline) on (offline metric: AUC, MAPE, NDCG) by (amount)]
[Validated it in (A/B test, holdout, backtest) over (period), moving (business metric) by (amount or direction)]
[Took (model or pipeline) to production with (Airflow, SageMaker, MLflow, a batch job, an API), retrained (how often), monitored for (drift or errors)]
[Designed or analyzed (N experiments) for (product teams), including (power analysis, guardrails, method used)]

[Previous job title], [Company]

[City, State] · [Mon YYYY] – [Mon YYYY]

[Replaced (rules, spreadsheet, manual process) with (model or analysis) used by (who), with (result)]
[Worked with (engineers, product, domain experts) to (define a metric, build features, get a model approved)]
[Shared a reusable asset: (feature table, evaluation framework, internal package) adopted by (N people or teams)]

Skills

Languages and data: [Python (pandas, NumPy, scikit-learn), SQL, Spark, R]
Modeling: [Gradient boosting, regression, time series, deep learning in PyTorch, causal inference, Bayesian methods]
Production: [Airflow, MLflow, Docker, a cloud ML service, feature stores, model monitoring]

Projects and Publications

[Paper or preprint title], [Venue or arXiv], [YYYY] · [Your role: first author, co-author]

[Public project] · [Link] · [Question, data, method and the honest result]

Education

[M.S. or Ph.D. in (statistics, computer science, physics, economics or your field)], [University], [YYYY] · [Thesis topic, if relevant]

[B.S. in (field)], [University], [YYYY]

Data science teams read a resume for evidence that your models left the notebook. Methods matter less than the trail: what business problem, what baseline you had to beat, how the model was validated, how it reached users or decisions, and what moved afterward. That is why Experience is the second section here, and why each of its bullet slots follows that trail.

The skills block follows, grouped into languages and data, modeling, and production, because the screen still checks for Python, SQL and the model families in the posting. After it comes a combined section for projects and publications. Research-leaning candidates fill it with papers and preprints; industry candidates use it for one or two pieces of public work, or remove it.

When you replace the brackets, set every metric beside something it beat (the previous model, a rule, a heuristic), since a number with no reference point tells a reviewer nothing. The completed version is the data scientist resume example.

Updated 2026-10-13.

How to fill in each section

Summary: problem type, shipped work, methods
Say which kind of data scientist you are in the first line: experimentation and product analytics, forecasting, recommendations, language models, risk. The posting usually reveals which it wants, and a mismatch is a common reason a capable candidate is passed over. The second sentence names one piece of work that shipped and what it moved. Finish with two methods or tools that match the ad, not a list of every library you have imported.
Experience: baseline, validation, deployment, effect
Write each model bullet as a short case: what it predicted, what it beat, how you proved it, and where it runs now. Offline metrics belong next to the baseline, then the online or business result. If a model never shipped, say what it informed instead, such as a pricing decision or a decision not to build. Collaboration with engineers belongs here too, because a hiring manager wants to know you can get work into production alongside others.
Skills: three layers, honest depth
Separate the block into the languages and data tools you work in, the model families you have shipped, and the tools you use to deploy and monitor. List model families rather than individual algorithms. Interview panels pick their questions from this block, so leave off methods you have only read about. If the posting mentions causal inference or experimentation, put that first on the modeling line; those roles screen hard for it.
Projects and Publications: depth or public proof
Researchers should list peer-reviewed papers and preprints with venue, year and authorship position, most relevant first. Industry candidates rarely need more than two lines: a public project with an honest evaluation, or a talk or post that explains a method well. Competition results help mainly early in a career. Remove the section entirely if neither applies; filler weakens the page more than a missing section does.
Education: degree, field and thesis
Advanced degrees are common in data science, so list the highest first, with the field and year. Add the thesis topic in a few words when it relates to the posting, for example time series or causal methods. Bootcamp graduates and people from other quantitative fields can list the program here and let the experience and projects above show the applied work. Grades help only in the first year or two after graduating.

See it filled in

The data scientist resume example uses this structure with a realistic candidate, with versions for entry, mid and senior level.

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Questions about the data scientist resume template

Should a data scientist resume lead with publications?

Only for research scientist roles or teams that publish. For product and applied machine learning roles, shipped work matters more, so Experience comes first and publications sit in the section near the end. Move them up if most of your record is academic.

How do I write a model bullet if the model was never deployed?

Say what the work informed and be clear about its status: the analysis that showed a feature would not pay off, or the prototype handed to engineering. An honest status reads better than implied production use that an interviewer will ask about.

What if most of my work was dashboards and reporting?

Then the data analyst resume template may describe you more accurately. If you are moving into modeling, keep this layout and add one or two projects where you built and evaluated a predictive model against a baseline.

Is one page enough for a data scientist with a PhD?

Two pages is usually acceptable with a doctorate and research output, as long as the first page holds the summary, most recent role and strongest results. Keep the full publication list for an academic CV and include only the relevant papers here.

Is it free?

Yes. The Word files and the Google Docs copy are free to download, edit and use for any application. There is no signup and no email.

Will it read correctly in an applicant tracking system?

The layout is one column with standard headings and real text: no tables, text boxes, icons, columns or images, which are the parts parsers most often misread. Keep it that way as you edit.

Word or Google Docs?

Use whichever you edit in. The Word file opens in Microsoft Word, Pages and LibreOffice. The Google Docs link asks you to make a copy in your own Drive; the original stays blank for the next person. Send the posting the format it asks for, usually .docx or PDF.

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