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Lead machine learning engineers and data scientists to build ML recommendations at-scale (e.g., via GAI-empowered embeddings, DNN, casual ML, Reinforcement Learning) for acquisition and cross-sell across LI lines of businesses with $20M annualized incremental impact. Sample projects include: - Personalized Next-Best-Action (NBA) Recommendations with insights (causal uplift modeling, RL);- Member propensity modeling; member-product fit recommendation (DNN, GAI-trained embedding over 200 million active Linkedin members);- Model-driven customer price elasticity model estimation.

I lead LinkedIn Talent Solutions go-to-market team, consisting of 10+ team members (including two frontline managers) to work with sales / marketing on customer life-cycle strategy via AI/ML model recommendation, scalable experiments and program incremental ROI measurement (causal inference) with more than $90M annualized incremental bookings in total. Sample projects include:- Customer churn / upsell predictions (with personalized insights) at scale, resulting +8% sales lift;- Lead score / propensity modeling;- Acquisition models (predicted LTV, conversion prediction at scale across different products and tiering services).

I led data analysts to drive product and marketing growth via data-driven experimentation, both through measuring & improving marketing programs / strategies as well as product features. Recent projects include:- Lead and drive cross-functional teams (consisting of engineers, UX researchers & designers and data scientists) to design and compare strategies to accelerate product adoption via A/B tests;- Partnered with 10+ cloud teams to quantify the causal impact of their programs (worth 10M+ investment), to optimize customer on-boarding / signup flows via web experiments; and to increase content engagement by 10% (overall) via email marketing;- Built and prototyped analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of GCP customer bases, advocating for changes where needed for both products development and marketing strategy and measuring the effectiveness via experimentation;- Published quarterly learning on A/B tests, built knowledge repository and organized education sessions.

- Led cross-functional teams to design, conduct and analyze lift tests to evaluate new advertising products (e.g., Messenger ads/Instagram Canvas/Ads delivery & ranking algorithms) success before launch in market;- Conducted product analysis to provide insights with engineering, PM and product marketing teams.

Provided marketers with best practices of planning out and measuring their advertising campaigns via Facebook ad products, based various methodologies include the design of experiments, attribution modeling (MTA) and causal modeling. More specifically,- Helped create research learning and agendas that drove tactical and strategic product decisions;- Conducted research using various methods including experiments, attribution modeling and other econometric modeling;- Analyzed large-scale user data to study ad campaign effectiveness (SQL, R);- Socialized/evangelized research findings generated by Ads and Consumer Research teams and shared them externally at industry conferences and workshops;- Presented and shared the Marketing Science measurement narratives in client and advertising agency meetings.

- Designed optimal field or lab experiments for new product evaluation and vet client campaigns recruited for the experiment;- Executed or oversaw the execution of experiments and analyzed experiment results;- Presented research results and make recommendations to the product team;- Collaborated with product marketing managers to integrate results in to sales and marketing materials.

- Ran experiments and data analysis in an NSF grant research project on social advertising and the economics of recommendation system. - Taught an advanced undergraduate course "Experimental Economics".

- Developed research projects in behavioral public economics, including utilizing behavioral economic theory and empirical findings to generate research questions, designed experiments and collected data to test these questions, analyzed data and reported these findings in academic conferences and journal articles. - Research topics include group decision making, tournaments and contests, public goods provision, tax evasion, norm compliance and cooperation in social dilemmas.

Taught in Behavioral Economics (master/Ph.D.level)

Taught approximately 150 undergraduate students in International Economics (3 years)