Resume template · Updated October 2026
Data scientist resume template
Short answer
Leslie suits most working data scientists because it prints experience before skills. Python, SQL and scikit-learn appear on nearly every data science resume, so what sets you apart is the experiments you ran and the decisions they changed. The fictional sample spent five years on subscriptions and health plans: faster A/B tests, a churn model the retention team uses, and a holdout study that ended a promotion.
Download the data scientist sample
Sofia is a fictional data scientist. Replace every detail with your own before you send it.
The files are made in your browser by Resumity's own exporters, from this sample. The builder opens it as your own resume: click any line to replace it. Free plan, no watermark.
Why Leslie suits a data scientist resume
Leslie puts work experience directly under the summary and ahead of the skills rows. A data science toolbox looks much the same from one candidate to the next, while the decisions your analysis changed do not, so letting the bullets come first plays to the strongest part of the page. The centred blue header is its only colour.
Second choice: FAANGPath. FAANGPath moves labelled skill rows above the jobs; choose it for modelling-heavy postings where the framework list is the first filter.
What goes in each section, in Leslie's order
Leslie prints its sections in a fixed order, the same in the PDF and the Word file. Here is that order for this sample, and what to put in each section.
- Professional Summary. Name the domain and the kind of problems you work on in one sentence (experiments, churn, pricing, forecasting), then how far you take the work: from test design to the readout, or from feature engineering to a model in use.
- Work Experience. Write each bullet as question, method, decision. "Ran a holdout study and showed the promotion only pulled orders forward; it was ended" beats "performed causal analysis". Keep at least one bullet about a model or analysis someone uses every week.
- Skills. Leslie prints labelled rows after the jobs. Put methods in Technical (experiment design, causal inference, gradient boosting, time-series forecasting) next to languages, and the warehouse, orchestration and notebooks in Tools.
- Education. Degree, field and school. A master's in statistics, economics or computer science is common in this field; a PhD can add its thesis topic in one line if it relates to the role.
The sample resume as text
The same content as the files above, so you can copy a line or compare it with your own.
Sofia Marchetti
Data Scientist, Experimentation and Retention
Philadelphia, PA | sofia.marchetti@example.com | (215) 555-0168 | linkedin.com/in/sample-sofia-marchetti | github.com/sample-sofia-marchetti
Data scientist with five years on consumer subscriptions and health plans, working on experiments, churn and forecasting. Designs the test or model in Python and SQL and writes the readout that product and finance teams act on.
Experience
Data Scientist, Pantry Path, Philadelphia, PA (08/2022 to Present)
- Designed and analysed 30 A/B tests on menus and checkout; adding CUPED variance reduction let most tests reach a decision in two weeks instead of four.
- Built a gradient-boosted churn model that ranks subscribers by risk each week; the retention team uses the scores to target pause offers.
- Ran a holdout study on a free-dessert promotion and showed it pulled orders forward rather than adding new ones; the promotion was ended.
- Wrote the experiment readout template (hypothesis, guardrail metrics, decision) now used by the whole analytics team.
Associate Data Scientist, Harborview Health Plans, Baltimore, MD (07/2020 to 07/2022)
- Forecast monthly call-centre volume by member segment with seasonal baselines and Prophet; staffing plans now start from the forecast.
- Built a logistic regression flagging members likely to miss a preventive screening, used to pick members for an outreach pilot.
- Cleaned and documented claims extracts in SQL so other analysts could reuse them.
Education
MS, Statistics, Delaware Valley University (05/2020)
BA, Economics, Lehigh Hills College (05/2018)
Skills
Python (pandas, scikit-learn, statsmodels), SQL, Experiment design, Causal inference, Gradient boosting, Time-series forecasting, Snowflake, dbt, Airflow, Jupyter, Git, Tableau, Writing for decision makers, Working with product managers
How to make the sample yours
Lead each bullet with the decision
Hiring managers want to know what changed because of your work. Rewrite the sample bullets so the outcome (a test reached a call, a promotion stopped, staffing changed) is in the first half of the sentence.
Name methods you can defend at a whiteboard
The sample mentions CUPED, holdout groups and gradient boosting. Use your own, and only ones you could explain to an interviewer, including when you would not use them.
Use relative numbers when the real ones are private
Test duration halved, retention up a few points in the treated group, forecast error down by a third: all of these work without revealing revenue.
Say what was yours in a team model
If three people built the churn model, say which part you owned: the features, the validation, the deployment or the weekly scoring job.
Swap the domain vocabulary
Subscriptions talk about churn and pauses; lending talks about default and approval rates; marketplaces talk about liquidity. Use the terms of the industry you are applying to where your work genuinely matches.
Keywords to keep where they are true
Terms this kind of resume is usually searched for. Use the posting's exact wording, and only for work you have done.
- data science
- Python
- SQL
- A/B testing
- experiment design
- causal inference
- statistical modelling
- machine learning
- scikit-learn
- churn prediction
- forecasting
- feature engineering
- Snowflake
- data storytelling
Mistakes to avoid
- A skills row with every algorithm from a course (SVM, k-means, random forests, naive Bayes). List the ones you have used on real data.
- Reporting model accuracy or AUC on its own. Add the baseline and what the business did with the model.
- Kaggle or course projects above paid work once you have a data science job.
- Phrases like "leveraged machine learning to drive insights" that name no question, method or result.
How the sample PDF reads in text extractors
We exported this sample with Resumity's PDF exporter in Chrome on 2026-10-29 and read the file back with three open-source text extractors. This shows whether software that turns a PDF into text can recover it; it is not a test of any commercial applicant tracking system. How we test templates.
| Extractor | Name, email, phone | Headings | Bullets intact |
|---|---|---|---|
| pdf.js 4.10.38 | All found | 4 of 4 | 7 of 7 |
| pdftotext (Xpdf) 4.00 | All found | 4 of 4 | 7 of 7 |
| pypdf 6.13.0 | All found | 4 of 4 | 7 of 7 |
Frequently asked questions
Should a data scientist link GitHub or Kaggle?
Link GitHub if it holds clean, documented analysis or a package you maintain. A Kaggle profile helps early in your career; once you have paid data science work, it matters much less than the bullets.
How do I describe a model that never reached production?
Say what it was for, how you validated it and why it stopped, if you know. A well-run analysis that showed a model was not worth deploying is still evidence of judgement.
Do I need a master's degree to be hired as a data scientist?
Many postings ask for one, but strong experience often stands in for it. List the degree you have plainly and let the experiments and models in your bullets carry the case.
How much statistics should show on the page?
Name the methods inside bullets where they did the work, such as a holdout, a regression or a forecasting model. A separate list of statistical tests adds little.
Can a data scientist resume run to two pages?
After about eight years, or with publications that matter for the role, yes. Five years of work, as in the sample, fits on one page.
Will applicant tracking systems read this Leslie PDF?
The sample PDF read back with its contact details, all 4 section headings and all 7 bullets intact in three open-source text extractors (pdf.js, pdftotext and pypdf). Commercial systems such as Workday, Greenhouse or Taleo were not tested, and each employer configures its own.
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