Norases Vesdapunt
Growth Data Science Tech Lead
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolNorases Vesdapunt is a Growth Data Science Tech Lead in Palo Alto with 12 years of experience blending data science, software engineering, and product-led growth. He built and scaled highly effective referral and social graph initiatives at Robinhood—creating the Free Stock program that drove millions of funded accounts—and later led growth and data at Slingshot Finance where fractional NFT referrals and performance marketing contributed to millions of signups and billions in volume. Technically fluent from large-scale entity resolution and social network mining to multi-armed bandit optimization (notably contributing bandit improvements in Yelp’s MOE), he pairs hands-on algorithmic work with team leadership and experimentation rigor. A Stanford PhD-trained researcher who shipped production systems at Google, Facebook, and Yelp, he’s comfortable moving between low-level systems and growth strategy while mentoring cross-functional teams.
11 years of coding experience
13 years of employment as a software developer
High School Diploma, High School Diploma at Satit KasetBachelor's Degree Computer Science, Bachelor's Degree Computer Science at Columbia EngineeringDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Stanford University
Github Skills (2)
python10
numpy9
Programming languages (1)
Github contributions (1)
Yelp/MOE
Jun 2014 - Aug 2014
A global, black box optimization engine for real world metric optimization.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:110 commits in 1 month
Contributions summary:Norases primarily worked on optimizing the multi-armed bandit system. Their contributions involved refactoring the codebase to replace deprecated threading methods with a new constant, refactoring and updating code to use a threading scheduling, and making updates and additions to the UCB1 and BLA multi-armed bandits. These changes suggest a focus on enhancing the performance and functionality of the underlying bandit algorithms within the project.
boxoptimizationblack-box-optimizationblack-boxespn
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