What does a good data scientist resume look like in 2026?

A strong 2026 data scientist resume ties each model to the decision it drove, reports both offline and online metrics, shows production deployment, and names the ML and data stack the job description lists. It is single-column and ATS-readable, usually one page, with business impact foregrounded over technique.

How is a data scientist resume different from a machine learning engineer resume?

Data scientist resumes weight statistics, experiment design, and the business decisions models inform; ML engineer resumes weight serving, MLOps, latency, and production infrastructure. The same project can be framed either way — read the JD and foreground the matching half.

Should I list Kaggle or tutorial projects?

One real production or research model outweighs several tutorial-grade notebooks. If you include personal projects, quantify the dataset, the metric, and any real-world use. For early-career resumes, a strong capstone or thesis with measurable results carries weight.

How do I show impact without revealing confidential numbers?

Use relative figures (a 6% conversion lift, AUC 0.87, 9 experiments) and describe the revenue base or user scale in rounded terms. You can demonstrate impact without exposing proprietary absolutes — never fabricate numbers to fill the gap.

Is this data scientist resume example ATS-friendly?

Yes. It uses a single-column layout, standard headings, grouped skills, and quantified, decision-linked bullets — the structure ATS parsers read reliably. Avoid embedding model plots or notebooks as images, which parsers cannot read.

Data Scientist · resume example

Data Scientist resume example.

A strong data scientist resume balances modeling with the decision it drove — the lift, the constraint, the ship — not just "built a model." Name your stack (Python, scikit-learn, SQL), distinguish offline from online metrics, and keep it single-column and ATS-readable. Below is a full sample plus how to build your own.

Score my resume free →Data Scientist optimizer guide

The sample

A full data scientist resume, top to bottom.

A realistic, ATS-friendly example you can model yours on. Names, companies, and numbers are illustrative — the structure and phrasing are what to copy.

Ravi Kapoor

Data Scientist — ML & Experimentation

San Jose, CA · ravikapoor.ml · github.com/ravikapoor · linkedin.com/in/ravikapoor

Summary

Data scientist with 5 years shipping models that move business metrics. Built a churn model that helped retention prioritize 12K at-risk users/quarter, designed 9 experiments tied to a $40M revenue base, and partners closely with engineering to ship to production. Strong in Python, scikit-learn, SQL, and causal inference.

Skills

ModelingPython · scikit-learn · XGBoost · PyTorch · feature engineering
Stats & experimentsA/B testing · causal inference · statsmodels · hypothesis testing
DataSQL · pandas · Spark · dbt
ProductionMLflow · Airflow · model monitoring

Experience

Data Scientist — Streamline Media
2021 — Present
San Jose, CA
- Shipped a gradient-boosted churn model (LightGBM, AUC 0.87) into a weekly batch pipeline; helped the retention team prioritize 12K at-risk users/quarter.
- Designed and read out 9 A/B tests across signup and checkout; one ranked-search experiment shipped a +6% conversion lift on a $40M revenue base.
- Re-scored the homepage recommendation model with session-level features; offline NDCG +9% and online CTR +4% on the holdout cohort.
- Built a reusable experimentation library that cut average test setup from 3 days to half a day for the analytics team.
Junior Data Scientist — Northpoint Analytics
2019 — 2021
Remote
- Built demand-forecasting models (Prophet + XGBoost) that cut inventory stockouts 14% for a retail client.
- Automated a feature pipeline in Airflow, removing ~8 hours/week of manual data prep.

Education

M.S. Statistics — University of California, Davis
2017 — 2019
Thesis: causal inference for observational marketing data

Certifications

Deep Learning Specialization (Coursera) · AWS Certified Machine Learning — Specialty

This is an illustrative sample, not a real individual. Use the structure, ordering, and bullet phrasing as a model — fill in your own true experience and numbers.

Write your own

How to write a data scientist resume.

01
Tie every model to a decision
AUC or F1 means nothing without "what decision does this drive?" Lead with the business action ("helped retention prioritize 12K users"), then the model and metric.
02
Distinguish offline from online metrics
Reporting both offline (NDCG, AUC) and online (CTR, conversion) signals mature DS practice. JDs that emphasize experimentation scan for this distinction.
03
Show production, not just notebooks
Models that shipped to a pipeline beat tutorial-grade notebooks. Name the deployment shape (batch, real-time) and the monitoring you added.
04
Name the stack the JD asks for
Python, scikit-learn, SQL, Spark, MLflow — surface the exact tools the posting lists where true. Distinguish DS (stats + ML) from ML-engineer (serving + MLOps) framing.
05
Quantify experiment design, not just results
Number of tests, the metric, the revenue base affected. "9 A/B tests; +6% conversion on a $40M base" shows rigor and scale.
06
Keep it single-column and ATS-readable
Standard headings, no embedded charts as images. A clean true-text PDF parses reliably and keeps your impact metrics visible.

Keywords ATS scans for

Data Scientist ATS keywords.

Work the ones that are genuinely true of you into your bullets and skills — not a keyword-stuffed block. Paste your resume + a job description into Resuvia to see which of these you're missing.

Data ScientistPythonscikit-learnXGBoostPyTorchSQLA/B testingcausal inferencefeature engineeringexperimentationmachine learningMLflowSparkstatisticsNLP

Common questions

Data Scientist resume questions, answered.

What does a good data scientist resume look like in 2026?
A strong 2026 data scientist resume ties each model to the decision it drove, reports both offline and online metrics, shows production deployment, and names the ML and data stack the job description lists. It is single-column and ATS-readable, usually one page, with business impact foregrounded over technique.
How is a data scientist resume different from a machine learning engineer resume?
Data scientist resumes weight statistics, experiment design, and the business decisions models inform; ML engineer resumes weight serving, MLOps, latency, and production infrastructure. The same project can be framed either way — read the JD and foreground the matching half.
Should I list Kaggle or tutorial projects?
One real production or research model outweighs several tutorial-grade notebooks. If you include personal projects, quantify the dataset, the metric, and any real-world use. For early-career resumes, a strong capstone or thesis with measurable results carries weight.
How do I show impact without revealing confidential numbers?
Use relative figures (a 6% conversion lift, AUC 0.87, 9 experiments) and describe the revenue base or user scale in rounded terms. You can demonstrate impact without exposing proprietary absolutes — never fabricate numbers to fill the gap.
Is this data scientist resume example ATS-friendly?
Yes. It uses a single-column layout, standard headings, grouped skills, and quantified, decision-linked bullets — the structure ATS parsers read reliably. Avoid embedding model plots or notebooks as images, which parsers cannot read.

More examples

Other resume examples.

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