Data Scientist Resume Example (2026) — ATS-Friendly

Data Scientist Resume Example

Models tied to revenue, retention, and decision speed

Data scientist resumes must connect modeling work to business outcomes — churn reduced, forecast accuracy improved, experiments won. This example pairs Python, SQL, and ML stack clarity with quantified impact and ATS keywords hiring managers search for in analytics and product teams.

ATS keywords for data scientist resumes

Work the terms that match the job description naturally into your resume so it ranks well in applicant tracking systems.

data scientist
Python
machine learning
SQL
scikit-learn
A/B testing
feature engineering
pandas
statistics
data visualization

Single-column, ATS-readable structure

Quantified, achievement-focused bullet points

Role-specific skills grouped for fast scanning

Raj Patel

raj.patel@email.com · +1 (415) 555-0163 · San Francisco, CA · https://linkedin.com/in/rajpatel · https://github.com/rajpatel

Professional summary

Data scientist with 6 years building models that move retention, revenue, and forecast accuracy. I pair Python and SQL with rigorous experimentation — shipping production ML features and clear recommendations stakeholders act on.

Skills

Modeling

Python, scikit-learn, XGBoost, Feature engineering, A/B testing

Data

SQL, pandas, Statistics, Data visualization, dbt

Professional experience

Data Scientist
Orbit Analytics — San Francisco, CA
Feb 2021 – Present
- Built a churn model that identified 8,500 at-risk accounts, reducing monthly churn by 12%.
- Improved demand forecast MAPE from 18% to 9% with a gradient-boosted ensemble pipeline.
- Designed and analysed 14 product experiments, winning 9 with statistically significant lifts.
Junior Data Scientist
Brightline Fintech — Oakland, CA
Aug 2018 – Jan 2021
- Deployed a fraud-scoring model that cut false positives by 28% while holding detection rate flat.
- Automated feature pipelines in Python that cut model refresh time from 6 hours to 45 minutes.
- Presented monthly insights to product leadership on cohort retention and pricing sensitivity.