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

PROFESSIONAL SUMMARY
E-commerce Data Scientist with [X] years optimizing online retail through machine learning. Expert in recommendation systems, dynamic pricing, and customer analytics. Proven track record increasing revenue by $[amount]M and conversion rates by [X%].

TECHNICAL SKILLS
E-commerce Specializations: Recommendation Systems, Customer Segmentation, Churn Prediction, Lifetime Value, Dynamic Pricing
ML/Analytics: Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, XGBoost
A/B Testing: Experimentation design, statistical analysis, causal inference
Analytics Tools: Google Analytics, Amplitude, Mixpanel, Segment
Cloud: AWS, GCP, Snowflake, BigQuery
Visualization: Tableau, Looker, Matplotlib

PROFESSIONAL EXPERIENCE

Data Scientist, E-commerce
[Company Name], [City, State]
[Start Date] – Present
• Built recommendation engine increasing GMV by [X%] ($[amount]M annually)
• Developed dynamic pricing model optimizing margins while maintaining [Y%] conversion
• Created customer segmentation identifying [X] high-value segments for targeted marketing
• Implemented churn prediction model enabling proactive retention reducing churn by [Y%]
• Conducted [number] A/B tests monthly optimizing product discovery, checkout, and merchandising
• Analyzed user behavior data for [X]M customers informing product roadmap decisions

E-commerce Data Scientist
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Built personalized search ranking improving click-through rate by [X%]
• Developed product recommendation system driving [Y%] of total revenue
• Created cart abandonment prediction model with targeted win-back campaigns
• Optimized email marketing campaigns increasing open rates by [X%] and conversion by [Y%]
• Analyzed cohort behavior identifying opportunities increasing LTV by [Z%]

KEY PROJECTS

Recommendation Engine
• Built collaborative filtering and content-based hybrid system
• Serves [X]M users with [Y] ms latency
• Increased average order value by [Z%] and items per cart by [W%]
• Technologies: Python, TensorFlow, Redis, AWS

Customer Lifetime Value Model
• Developed probabilistic model predicting 12-month LTV
• Enabled efficient customer acquisition spend allocation
• Improved marketing ROI by [X%] through better targeting
• Integrated with marketing automation platform

Search Ranking Optimization
• Built learning-to-rank model personalizing search results
• Improved conversion rate from search by [X%]
• Reduced time to purchase by [Y%]
• Deployed using Elasticsearch and real-time feature serving

Dynamic Pricing System
• Created price optimization model considering demand elasticity and competition
• A/B tested across [X] SKUs and [Y] categories
• Increased margin by [Z%] while maintaining volume

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

CERTIFICATIONS
• [Google Analytics Individual Qualification], [Year]
• [AWS Certified Machine Learning], [Year]

E-COMMERCE EXPERTISE
• Customer Journey: Awareness, consideration, purchase, retention, advocacy
• Funnel Optimization: Landing pages, product pages, cart, checkout
• Metrics: CAC, LTV, conversion rate, cart abandonment, AOV, RPU
• Platforms: Shopify, Magento, BigCommerce, WooCommerce