Priya Raman
Data scientist with 5 years of experience building machine learning models for pricing, churn and demand forecasting. Developed a churn model that helped retain an estimated $2.4M in annual revenue. Skilled in Python, SQL and experiment design, and comfortable explaining results to non-technical stakeholders.
- Built a gradient-boosted churn model (AUC 0.86) used to target retention offers, helping retain an estimated $2.4M in annual premiums
- Designed and analyzed 15+ A/B tests on pricing and messaging for the digital sales team
- Deployed models to production on AWS SageMaker with automated weekly retraining
- Presented findings monthly to product and executive leadership
- Built a demand forecasting model that reduced forecast error (MAPE) from 24% to 13% for 1,200 SKUs
- Automated weekly reporting in Python and SQL, saving the team 10 hours per week
- Created Tableau dashboards used by 40+ merchandising and supply chain staff
- Thesis: Bayesian methods for sparse demand forecasting
Python (pandas • Scikit-learn • XGBoost) • SQL • PyTorch • Statistics • A/B testing • Machine learning • Time series forecasting • Feature engineering • Spark • AWS SageMaker • Tableau • Git
Data Scientist resume summary examples
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Data Scientist resume bullet points
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- Built a churn prediction model (AUC 0.86) that helped retain an estimated $2.4M in annual revenue
- Reduced demand forecast error from 24% to 13% MAPE across 1,200 products
- Designed and analyzed 15+ A/B tests, informing pricing changes that raised conversion by 6%
- Developed a recommendation engine that increased average order value by 9%
- Deployed and monitored models in production on AWS SageMaker with automated retraining
- Engineered features from 50M+ rows of transaction data using Spark and SQL
- Automated reporting pipelines in Python, saving analysts 10 hours per week
- Translated model results into clear recommendations for executives and product managers
- Built an anomaly detection model that flagged fraudulent claims 3 days faster on average
- Mentored 2 junior analysts in Python, statistics and experiment design
Top skills for a data scientist resume
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ATS keywords for data scientist resumes
These terms appear often in data scientist job descriptions. Use the ones that truthfully describe your experience, in the same words the posting uses. To see exactly which keywords your resume is missing for a specific job, try the job description matcher.
How to write a data scientist resume
1. Tie models to business outcomes
Accuracy metrics matter, but hiring managers care more about impact: revenue retained, costs saved or decisions improved. Pair a model metric with its business result where you can.
2. Be honest about estimated impact
If impact was estimated rather than directly measured, say "estimated." Overstated results are easy to question in a technical interview.
3. Show the full workflow
Mention data collection, feature engineering, modeling, deployment and communication. Teams want data scientists who can take work beyond a notebook.
4. Link to real projects
A GitHub repo or write-up with clean code and a clear README helps, especially early in your career. Only show work you did yourself or clearly credit collaborators.
5. List tools you can be interviewed on
Keep your skills section focused on languages and libraries you use confidently. A long list of tools you barely know can hurt you in technical screens.
New to resumes? Read our full guide on how to write a resume or browse ATS-friendly resume templates.
Frequently asked questions
What should a data scientist resume include?
A short summary, a focused technical skills section, experience bullets that show models and business impact, education and relevant projects or publications.
Do I need a master's degree to be a data scientist?
Many data scientists have an M.S. or Ph.D., but it is not always required. Strong projects, analytics experience and solid statistics skills can also get you hired.
How do I write a data scientist resume with no experience?
Lead with 2–3 substantial projects using real datasets. Describe the question, methods, tools and results, and include internships, research or Kaggle work.
Should I list publications?
Yes, if they are relevant. Add a short Publications section for research roles; for industry roles, keep it brief and focus on applied work.
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