Palo Alto, California, United States
• Designed data-driven sourcing system and led team developing system; system has identified dozens of top-of-funnel prospects • Established methodology for evaluating alternative data providers and ran user trials to reach purchasing decisions • Developed processes and tools for analyzing firm's pipeline and portfolio to inform investment decisions
• Led ML team efforts to quantify and monitor performance: e.g., designed metrics and built tools to track Afresh’s reduction of waste and to detect and diagnose software and data quality issues • Managed projects including (a) a system that balances weekly orders to meet distribution center capacity limits; (b) a refactoring of Afresh’s codebase that increased efficiency by 15%; and (c) a system that tracks inventory levels for produce items based on sales and shipments • Built deep learning model predicting demand for fresh produce, improving performance by 20% • Interviewed candidates for engineering roles, including leadership positions on the ML team • Developed company-wide IT security policies and procedures needed for SOC 2 compliance
• Developed model to classify the source of build failures; increased accuracy from 20% to 98.8% • Created system for triaging failures based on their classification; used by thousands of Microsoft developers
• Demonstrated effectiveness of a signal (i.e., a user action) as a proxy for ad conversions using MySQL- and R-based data analysis; integrated the signal into the ads targeting system • Trained machine-learning models to compare the predictive strength of signals