Reducing Data Pipeline Latency to Enable Real-Time Decision Loops
Redesigned batch pipeline to streaming architecture, reducing data lag from 2-3 days to <2 hours and enabling new decision workflows
Product and decision-science leader owning platforms that power AI-driven drug discovery. Lead roadmaps, discovery, and adoption for decision products that cut cycle time, reduce uncertainty, and standardize high-stakes portfolio trade-offs. Heavily involved in AI product adoption and building applied AI/agentic workflows to turn model capability into measurable delivery leverage.

Redesigned batch pipeline to streaming architecture, reducing data lag from 2-3 days to <2 hours and enabling new decision workflows
Designed evaluation framework combining offline validation with online performance tracking to de-risk multimillion-dollar compound selection decisions
Quantitative analysis guiding $250k+ partnership decision — ROI modeling revealed internal model needed 50× improvement, recommended hybrid approach balancing near-term progress with long-term IP
Designed decision framework cutting R&D cycle time 20% by pre-defining success criteria and pivot triggers — applied academic experimental design to product strategy
Led 6-week crisis response resolving data integrity failure — transformed incident into governance framework reducing future incidents from 3 to 0