Sarah Chen

About Sarah

Sarah Chen is a lead data scientist at DataScrap Studio with a background in computer science and statistics. She has been working with web data extraction technologies for over 8 years, helping businesses leverage the power of external data for competitive intelligence and market analysis.

Prior to joining DataScrap Studio, Sarah worked as a data engineer at Amazon, where she developed automated data pipelines for product catalog enrichment and competitive pricing analysis. She also served as a consultant for several e-commerce startups, helping them build data-driven decision-making frameworks.

Expertise

Sarah specializes in:

• Creating robust web scraping solutions that adapt to site changes
• Developing ETL pipelines for large-scale data processing
• Building machine learning models for product matching and categorization
• Designing dashboards and visualizations for market intelligence
• Training teams on data-driven decision making

Education

• M.S. in Computer Science, Stanford University
• B.S. in Statistics, University of California, Berkeley

Speaking and Publications

Sarah regularly speaks at data science conferences and has published several articles on web scraping techniques and ethical data collection. Her recent publications include:

• “Ethical Web Scraping: Best Practices for Responsible Data Collection” (2025)
• “Building Resilient Data Pipelines for E-commerce Intelligence” (2023)
• “Machine Learning Approaches to Product Matching Across Marketplaces” (2022)

When not coding or writing about data, Sarah enjoys hiking the trails of Northern California and experimenting with new cooking recipes.