Data Scientist Resume Example — Netflix
The data scientist resume that got hired at Netflix. Real bullets with model metrics, Python/SQL skills, and ATS-optimized format. Free to copy.
ATS Score: 95/100
This resume passes most Applicant Tracking Systems
Senior Data Scientist with 6 years specializing in recommendation systems and ML at scale. Built personalized content ranking model serving 260M+ subscribers at Hulu (patent pending). Reduced churn by 14% using causal ML. Published 2 papers on collaborative filtering at RecSys 2023. Expert in Python, PyTorch, and large-scale A/B experimentation.
This resume uses the Bookmark template — available free in ResumeLens
What Makes This Resume Work
Hover each callout to understand the strategy behind each section.
Model Metrics, Not Just Job Titles
Opens with 'recommendation model serving 260M subscribers' — a specific scale signal that immediately tells Netflix recruiters you've worked at comparable complexity. Always include the scale of your systems.
Business Impact, Not Just Accuracy
Goes beyond model accuracy: 'recommendation engine → +3.1% watch time across 260M users'. Netflix cares about engagement metrics, not F1 scores. Translate model performance into business outcomes.
Stack Matches Netflix's JD
Lists Python, PyTorch, Spark, Airflow, and A/B testing — exactly what Netflix ML JDs specify. Also includes SQL and Tableau, showing cross-functional data literacy, not just model building.
Publication as a Differentiator
Mentions a published paper in the summary and education section. For data science at top-tier companies, research experience and publications add significant credibility beyond job titles.
Top ATS Keywords Used
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Open in Resume Builder →✓Why This Data Scientist Resume Works
- Summary states model scale (260M users) — immediately communicates seniority and system complexity
- Every model mentioned has a business KPI: watch time, churn reduction, revenue impact
- Explicitly lists experiment design and A/B testing — Netflix runs hundreds of experiments simultaneously
- Python, PyTorch, Spark, and SQL all present — covers the full data science stack
- Publications demonstrate ability to do novel research, not just apply known methods
Full Resume Text — Free to Copy
Contact
Jordan Kim
Senior Data Scientist
jordan.kim@email.com
+1 (650) 555-0318
Los Altos, CA
linkedin.com/in/jordankim-ds
github.com/jordankim-ml
Professional Summary
Senior Data Scientist with 6 years specializing in recommendation systems and ML at scale. Built personalized content ranking model serving 260M+ subscribers at Hulu (patent pending). Reduced churn by 14% using causal ML. Published 2 papers on collaborative filtering at RecSys 2023. Expert in Python, PyTorch, and large-scale A/B experimentation.
Work Experience
Senior Data Scientist — Personalization
Hulu · Los Angeles, CA
- •Architected two-tower deep learning recommendation model (PyTorch) serving 47M daily active users; lifted content engagement rate by 11% and watch time by 3.1% ($18M incremental ad revenue annually)
- •Designed and ran 140+ A/B experiments in 2023 across personalization, content display, and notification triggers using Hulu's internal experimentation framework (Statsig)
- •Built churn prediction model combining causal ML (uplift modeling) and time-series features; identified 2.3M at-risk subscribers per month — targeted interventions reduced quarterly churn by 14%
- •Led cross-functional working group (Data Science, Engineering, Product) to migrate ad hoc Jupyter notebooks to production ML pipeline on Apache Airflow; reduced model deployment cycle from 3 weeks to 4 days
- •Mentored 3 junior data scientists; defined team's ML coding standards and peer review process adopted org-wide
Data Scientist
Spotify · New York, NY
- •Developed podcast recommendation model (matrix factorization + content embeddings) that increased podcast stream starts by 23% and reduced skip rate by 9% for 85M users
- •Collaborated with Audio ML team to integrate acoustic features (tempo, energy, valence) into playlist generation model; improved user-reported satisfaction score by 0.4 points (5-point scale)
- •Built real-time feature store using Redis + Apache Spark to serve 1,200 model features with <50ms latency at 180K req/sec
- •Conducted power analyses and designed 60+ experiments; created internal guide on experiment pitfalls (novelty effect, SUTVA violations) used as onboarding material by 12 new hires
Junior Data Scientist
Wayfair · Boston, MA
- •Built product similarity model using image embeddings (ResNet) + tabular features that powered 'Customers Also Bought' widget; contributed to 7% lift in average order value
- •Created automated anomaly detection for supply chain inventory levels using LSTM time-series model, reducing stockout incidents by 22% quarter-on-quarter
Skills
Education
M.S. Computer Science — Machine Learning
Carnegie Mellon University
May 2018 · GPA 3.92
B.S. Statistics & Computer Science
University of Michigan
May 2016 · GPA 3.85
Projects
RecSys 2023 — Published Paper
PyTorch, Causal ML, Collaborative Filtering
'Debiasing Implicit Feedback in Streaming Recommendation Systems' — presented at ACM RecSys 2023. 80+ citations.
scholar.google.com/jordankim
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