My recent work focuses on experimentation & measurement, incrementality analysis, and portfolio optimization—building ranking and decision policies that determine what to show, when to show it, and to whom, while rigorously measuring true incremental value. Most recently, I led a randomized portfolio ad placement optimization experiment, ranking products from an acquired and acquiring company by expected NPV to drive in-app ad sequencing, and measured an ~4% lift in incremental realized value through test-control experimentation.
Across roles, I’ve delivered impact at scale, including:
Driving multi-million-dollar annualized value through experimentation-led personalization and portfolio optimization initiatives
Improving marketing addressability and response through propensity and cross-sell models, expanding eligible customer populations by double-digit percentages
Supporting regulatory-facing analytics and model monitoring for large consumer credit portfolios, partnering with business and risk teams to address audit and compliance requirements
I’ve led and mentored teams, collaborated closely with product and engineering, and helped organizations move from intuition-driven decisions to experiment-validated strategies grounded in clear metrics and causal reasoning.
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Prior to becoming a data scientist, I worked in biomedical research, management engineering and healthcare consulting industries with experience in developing computational algorithms and mobile applications. Received Bachelor’s degree in Civil engineering in 2005 from Indian Institute of Technology, Roorkee; Master’s degree in Computational science from Massachusetts Institute of Technology in 2006; PhD degree in Biomedical engineering from the University of Western Australia in 2015.