Summary
Bat Sukhbaatar is a quantitative researcher with nine years of applied data science experience, currently building models at a proprietary trading firm after roles at Selector and FanAI. A UCLA Statistics graduate and active Kaggle competitor, he blends rigorous statistical foundations with practical machine-learning—ensemble methods, non-linear transforms, and careful cross-validation are core strengths. He has a track record translating messy SQL and multi-format datasets into actionable analytics for startups, healthcare, law enforcement, and entertainment clients. Known for pairing disciplined model validation with effective team communication, he also brings an unusually product-minded focus for a researcher, aiming to turn experiments into deployable, business-impacting systems.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science, Statistics, 3.65, Bachelor of Science, Statistics, 3.65 at University of California, Los Angeles
English, Mongolian