Models, experiments, and insights that move the business forward.
Data scientist resume example with ML model results, A/B test wins, and the Python/SQL keywords ATS systems look for. Tailor it to any job with AI on LumiCV.
Built churn prediction model (XGBoost, 87% precision) that identified at-risk customers 14 days earlier - retention team acted on scores and recovered $1.4M ARR in Q3.
Designed and ran 60+ A/B tests (pricing, onboarding, email) using Python + Statsig; shipped 18 winners that collectively added 9 pp to trial-to-paid conversion.
Reduced ETL pipeline runtime from 6 hours to 22 minutes by rewriting PySpark jobs with partition pushdown and broadcasting small lookup tables - saving $8K/month in Databricks costs.
Each bullet: strong verb → specific action → quantified result. Copy the structure, not the words.
Built churn prediction model (XGBoost, 87% precision) that identified at-risk customers 14 days earlier - retention team acted on scores and recovered $1.4M ARR in Q3.
Designed and ran 60+ A/B tests (pricing, onboarding, email) using Python + Statsig; shipped 18 winners that collectively added 9 pp to trial-to-paid conversion.
Reduced ETL pipeline runtime from 6 hours to 22 minutes by rewriting PySpark jobs with partition pushdown and broadcasting small lookup tables - saving $8K/month in Databricks costs.
How LumiCV helps: Paste any job description and our AI rewrites your bullets to match the role's exact keywords. Every template is ATS-parseable out of the box.
These terms appear most frequently in Data Scientist job descriptions. Missing them means your resume won't surface.
Data science resumes need to demonstrate both analytical depth and business impact. It's not enough to say you 'built an ML model' - you need to state the baseline, the improvement, and the business outcome (revenue lifted, cost avoided, churn reduced).
The best data scientist resumes also show a full workflow mindset: data collection, cleaning, feature engineering, model selection, evaluation, deployment, and monitoring. Candidates who show they've taken a model all the way to production stand out from those who hand work off to an MLOps team.
ATS keywords to include: Python, pandas, scikit-learn, TensorFlow or PyTorch, SQL, Spark for big data roles, A/B testing, statistical significance, and cloud ML platforms (SageMaker, Vertex AI, Azure ML). For senior roles, add 'stakeholder communication' and 'roadmap' to signal leadership.
A strong Data Scientist resume should include: a concise professional summary, quantified experience bullets showing impact, a skills section with relevant keywords, and clean ATS-parseable formatting. See the example bullets above for the level of specificity hiring managers expect.
Use a single-column or ATS-safe two-column template, include role-specific keywords naturally in your bullets, avoid tables and text boxes, and export as a real-text PDF (not an image). LumiCV's templates are parse-tested: each is rendered to PDF and verified with a machine parser to recover every field in the correct reading order.
Key ATS keywords for a Data Scientist resume include: Python, SQL, pandas, scikit-learn, TensorFlow, PyTorch, A/B testing, statistics, machine learning, Spark, Tableau, R. Use these naturally throughout your experience bullets and skills section - keyword stuffing in a hidden section will not work on modern ATS systems.
For most Data Scientist roles, one page is ideal for under 7 years of experience. Two pages are appropriate for senior roles with 10+ years. Beyond two pages is rarely justified. Cut anything that doesn't directly support why you're qualified for the role you're targeting.
Yes. LumiCV's free plan includes all 11 country formats and 3 ATS templates and lets you export your resume as a PDF. Pro (from $8.25/month billed yearly) adds 14 more templates and unlimited AI tailoring - paste a job description and LumiCV rewrites your bullets to match the role's keywords.
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