Industry Experience
Alongside my academic career, I have worked in industry on data-driven growth and optimization problems. My industry work centers on applying rigorous statistical and machine learning methods to real-world business challenges — from Life-Time-Value modeling and budget allocation to large-scale A/B testing and campaign optimization.
2021 – 2024Senior Quantitative Researcher
Developed and deployed statistical and machine learning methods to optimize advertising products at scale. Worked at the intersection of causal inference, experimentation, and large-scale optimization to improve advertiser outcomes across Meta's platforms.
- –Budget allocation optimization across advertising campaigns and channels
- –Campaign performance modeling and optimization using large-scale behavioral data
- –Design and analysis of A/B tests and sequential experimentation frameworks
- –Life-Time-Value (LTV) prediction and optimization for advertiser bidding strategies
- –Causal inference methods for measuring ad effectiveness
2016 – 2021Head of Growth Data Science
Led the Growth Data Science team, building the data-driven infrastructure and modeling capabilities behind Lightricks' user acquisition and monetization strategy. Translated statistical methodology into actionable growth levers for a fast-scaling consumer app company.
- –Life-Time-Value (LTV) modeling to guide user acquisition investment decisions
- –Budget allocation optimization across paid marketing channels (iOS, Android, social, search)
- –End-to-end A/B testing platform design, including sequential testing and early-stopping procedures
- –Campaign optimization models for improving ROAS across acquisition funnels
- –Churn prediction and retention modeling to inform subscription pricing and product decisions
- –Built and mentored the growth data science function from the ground up
2010 – 2012Researcher
Applied statistical and mathematical modeling to industrial problems, including work on adaptive imaging acquisition methods.