Resume Example
Data Scientist Resume Example
Focus on modeling, experimentation, and business outcomes. This sample highlights predictive impact and data rigor.
Modern Minimal
Clean sidebar layout with navy accent. Great for tech and finance roles.
Keywords
Sample bullets
- Built a churn model (AUC 0.87) that drove targeted outreach and cut churn by 9%.
- Designed pricing A/B tests that lifted ARPU by 6% while maintaining conversion.
- Automated feature pipelines that reduced model refresh time from 8 hours to 2 hours.
Soft skills
- Data storytelling
- Experimental design
- Stakeholder communication
- Analytical rigor
Certifications
- AWS ML Specialty
- Google Cloud Professional Data Engineer
- TensorFlow Developer Certificate
Why this works
- Demonstrates model impact tied to retention or revenue.
- Highlights experimentation discipline and statistical rigor.
- Shows production-ready data workflows.
Step-by-Step Guide
How to Write a Data Scientist Resume
Lead with business impact
Data science is valuable when it drives decisions. Your summary should connect your models and analyses to business outcomes: revenue increased, costs reduced, or products improved. Position yourself as someone who solves business problems with data.
Quantify model performance and impact
Include both technical metrics (accuracy, precision/recall, AUC) and business impact. 'Built recommendation engine with 0.85 AUC that increased average order value 18%' shows both technical rigor and business value.
Show your technical depth
List languages (Python, R, SQL), ML frameworks (scikit-learn, TensorFlow, PyTorch), statistical methods, and cloud ML services (SageMaker, Vertex AI). Include both classical ML and deep learning if applicable.
Demonstrate end-to-end ownership
Show you can take projects from problem framing through deployment. Include examples of models in production, A/B tests run, or dashboards delivering ongoing value. Implementation matters as much as modeling.
Highlight collaboration and communication
Data scientists work with stakeholders who don't speak statistics. Include examples of presenting to executives, translating findings for non-technical audiences, or partnering with engineers on production systems.
Summary Examples
Good vs. Bad Resume Summaries
“Data scientist with 6 years building ML models that drive business decisions. Developed fraud detection system saving $4M annually with 95% precision. Expert in Python, deep learning, and production ML systems.”
Clear impact ($4M), technical metric (95% precision), and specific expertise areas.
“Data scientist with machine learning expertise and strong statistical background. Passionate about using data to solve complex problems.”
Generic skills without evidence. No specific projects, metrics, or business outcomes.
“Applied scientist specializing in NLP and recommendation systems. Built personalization engine serving 10M users that increased engagement 32%. Published research on efficient transformer fine-tuning.”
Specific domains (NLP, recs), impressive scale (10M users), clear outcome, and differentiating research.
“Data-driven professional skilled in Python, SQL, and machine learning algorithms. Team player with strong analytical abilities.”
Basic skills expected of all data scientists. No projects, no impact, no specialization.
Action Verbs
Power Words for Data Scientist Resumes
Common Mistakes
What to Avoid
- ✗Focusing only on modeling without showing business impact
- ✗Missing production experience—models that aren't deployed have limited value
- ✗Listing algorithms without context about problems solved
- ✗Not including both technical metrics and business outcomes
- ✗Omitting collaboration with stakeholders and cross-functional teams
- ✗Being vague about model scale, performance, and real-world impact
Salary ranges
| Level | US | EU | Canada |
|---|---|---|---|
| Entry | USD 100,000-120,000 | EUR 55,000-75,000 | CAD 85,000-110,000 |
| Mid | USD 120,000-172,000 | EUR 75,000-100,000 | CAD 110,000-140,000 |
| Senior | USD 172,000-250,000 | EUR 100,000-140,000 | CAD 140,000-180,000 |
Market themes
- Deep learning mentions doubled YoY
- GenAI/LLM familiarity expected
- Degrees required in ~70% of postings
US hot markets
- New York
- San Francisco
- Seattle
- Boston
- Chicago
EU hot markets
- London
- Amsterdam
- Berlin
- Munich
Canada hot markets
- Toronto
- Vancouver
- Calgary
FAQ
Common questions about this role
What should data scientists highlight?
Model performance, experimentation, and real business outcomes.
How do data scientists show value?
Tie model lift and experiments to revenue, retention, or efficiency gains.
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