[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
Applied Data Scientist with [X] years solving real-world business problems through machine learning. Proven ability to translate business requirements into technical solutions generating $[amount]M in value. Expert in end-to-end model development from problem definition to production deployment and monitoring.

TECHNICAL SKILLS
Programming: Python, R, SQL
ML Frameworks: Scikit-learn, XGBoost, TensorFlow, PyTorch
Business Tools: Tableau, Excel, Google Analytics, Mixpanel
Cloud: AWS (SageMaker, Lambda, S3), GCP, Azure
Specializations: Predictive Modeling, Customer Analytics, Optimization, Forecasting
MLOps: Docker, Git, CI/CD, Model Monitoring

PROFESSIONAL EXPERIENCE

Applied Data Scientist / Data Scientist
[Company Name], [City, State]
[Start Date] – Present
• Partner with [business teams] to identify high-impact opportunities and define ML solutions
• Built customer lifetime value model increasing marketing ROI by [X%] and revenue by $[amount]M
• Developed dynamic pricing model optimizing margins while maintaining [X%] conversion rate
• Created demand forecasting system reducing inventory costs by [X%] ($[amount] annually)
• Collaborate with engineering to deploy [number] models to production serving [X]M users
• Present findings to stakeholders translating technical concepts for non-technical audiences

Applied Data Scientist
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Analyzed customer behavior data identifying $[amount]M in revenue opportunities
• Built churn prediction model with [X%] precision enabling proactive retention efforts
• Conducted [number] A/B tests optimizing [key metrics] and increasing conversion by [Y%]
• Automated reporting workflows saving [X] hours per week across [Y] teams
• Mentored [number] analysts on machine learning best practices

Data Scientist
[Earlier Company], [City, State]
[Start Date] – [End Date]
• Developed recommendation engine increasing cross-sell rate by [X%]
• Created segmentation model enabling targeted marketing campaigns
• Built dashboards tracking model performance and business KPIs

KEY PROJECTS & BUSINESS IMPACT

Customer Segmentation & Targeting
• Problem: Generic marketing campaigns with low conversion rates
• Solution: Built clustering model identifying [X] distinct customer segments
• Impact: Increased campaign ROI by [Y%] and revenue by $[amount]

Predictive Maintenance System
• Problem: Unexpected equipment failures causing $[amount] in annual losses
• Solution: Developed ML model predicting failures [X] days in advance with [Y%] accuracy
• Impact: Reduced downtime by [Z%] saving $[amount] annually

Sales Forecasting Model
• Problem: Inaccurate forecasts leading to inventory issues
• Solution: Built time series model with [X%] MAPE incorporating [factors]
• Impact: Improved forecast accuracy by [Y%] reducing stockouts by [Z%]

EDUCATION
[Degree Name], [Major]
[University Name], [City, State]
Graduation: [Month Year]

CERTIFICATIONS
• [Business Analytics or Data Science Certification], [Year]
• [Cloud Certification], [Year]

BUSINESS DOMAIN EXPERTISE
• [Industry-specific knowledge: Retail, Finance, Healthcare, etc.]
• [Functional expertise: Marketing, Operations, Supply Chain, etc.]
• Strong understanding of business metrics: CAC, LTV, Churn Rate, Conversion Funnel