Data science resumes often fall into one of two traps: either they read like an academic CV full of techniques nobody asked about, or they list tools without showing what those tools produced. Hiring managers want to see that you can take a business problem, frame it statistically, build something that works, and measure whether it mattered. Here's a data scientist resume built around that structure, with the ATS keywords that get it past the first filter.
Sample Resume
PRIYA NAKAMURA
Chicago, IL | priya.nakamura@email.com | github.com/priyanakamura
SUMMARY
Data scientist with 4 years of experience building production ML models for revenue forecasting and customer segmentation. Shipped models serving 50M+ predictions/month in Python and TensorFlow. Background in statistics and experiment design.
EXPERIENCE
Senior Data Scientist — Meridian Commerce, Chicago, IL
Aug 2023 – Present
• Built gradient-boosted revenue forecasting model (XGBoost + feature store) that reduced quarterly forecast error from 18% to 6%, directly informing $120M in inventory planning decisions
• Designed and analyzed 24 A/B tests across checkout flow, identifying 3 changes that increased conversion rate by 11% ($3.2M annual impact)
• Developed NLP pipeline using spaCy and BERT fine-tuning to classify 400K monthly customer support tickets, reducing manual triage time by 65%
• Created automated data pipeline in Airflow processing 8TB of clickstream data daily for the recommendation engine
Data Scientist — Forecast Labs, Boston, MA
Jun 2021 – Jul 2023
• Trained deep learning model (TensorFlow/Keras) for demand prediction across 12K SKUs, improving accuracy by 23% over the legacy linear regression baseline
• Built customer churn prediction model using scikit-learn (random forest + logistic regression ensemble) with 0.89 AUC, enabling proactive retention outreach to 15K at-risk accounts
• Wrote SQL queries and pandas pipelines to clean, transform, and join 14 data sources into unified analytics warehouse
• Presented statistical findings to non-technical stakeholders in weekly business reviews, translating model outputs into actionable recommendations
SKILLS
Languages: Python, SQL, R
ML/DL: TensorFlow, PyTorch, scikit-learn, XGBoost, Keras
Data: pandas, NumPy, Spark, Airflow, dbt
NLP: spaCy, Hugging Face Transformers, BERT, NLTK
Statistics: A/B testing, Bayesian inference, regression analysis, hypothesis testing
Visualization: Matplotlib, Plotly, Tableau, Looker
EDUCATION
M.S. Statistics — University of Chicago, 2021
B.S. Mathematics — University of Michigan, 2019
Power Bullet Points for Data Scientists
The best DS bullets connect a method to a metric. Name the technique, name the scale, name the result.
- Built revenue forecasting model (XGBoost + feature store) that reduced quarterly forecast error from 18% to 6%, informing $120M in inventory planning
- Designed and analyzed 24 A/B tests across checkout flow, identifying changes that increased conversion by 11% ($3.2M annual impact)
- Developed NLP classification pipeline using BERT fine-tuning to categorize 400K monthly support tickets, reducing manual triage by 65%
- Trained demand prediction model (TensorFlow/Keras) across 12K SKUs, improving accuracy by 23% over legacy linear regression baseline
- Built customer churn model (random forest + logistic regression ensemble) with 0.89 AUC, enabling proactive retention for 15K at-risk accounts
- Created automated Airflow pipeline processing 8TB of daily clickstream data for the recommendation engine, reducing data freshness lag from 12 hours to 45 minutes
- Translated model outputs into business recommendations for non-technical stakeholders across weekly reviews, directly influencing 3 product pivots
ATS Keywords
Include these keywords naturally throughout your resume:
Python
SQL
machine learning
TensorFlow
statistics
A/B testing
data pipelines
NLP
deep learning
pandas
scikit-learn
Tips for Data Scientist Resumes
- Name the model, name the metric, name the impact. "Built a machine learning model" is worthless. "Built XGBoost revenue forecasting model that reduced forecast error from 18% to 6%" tells the reader exactly what you know and what it did. Be specific about architectures and evaluation metrics.
- Separate ML skills from data engineering skills. Many DS roles expect both, but they're evaluated differently. Put TensorFlow, scikit-learn, and statistical methods under ML. Put Airflow, Spark, and SQL under data engineering. This helps ATS systems match you to the right keywords.
- Show you can communicate results, not just produce them. Include at least one bullet about presenting findings, translating model outputs for business stakeholders, or influencing a decision. DS roles increasingly require this, and hiring managers look for it.
- Don't bury your production experience. If your model runs in production serving real predictions, say so and include the scale. "Serving 50M predictions/month" signals production maturity that Jupyter-only candidates can't match.
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