Summary
Santona Tuli is a measurement sciences and data engineering leader with a decade of experience building end-to-end ML and data pipelines for scale, from CERN-sized physics experiments to enterprise ML platforms. She blends rigorous scientific methods—developed measuring rare particle signals at the LHC—with product-minded delivery of feature platforms and orchestration tooling (Airflow, stream-first pipelines) to make data workflows reliable and explainable. At Unity and Upsolver she led teams that deliver training data, inference features and the infrastructure that powers them; at Astronomer she focused on how people actually use Airflow to simplify and extend its ecosystem. Santona excels at strategic featurization, explainable ML, and validation frameworks that reduce signal loss and increase trust in models. She is as comfortable translating complex technical tradeoffs for executives as she is designing Monte Carlo–driven tests for terabytes of sensor data. Based in the DC–Baltimore area, she pairs a PhD in physics with practical product leadership to bridge research-grade analysis and production-grade systems.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Physics, Nuclear Sciences, Quantum Chromodynamics, Data, Doctor of Philosophy (Ph.D.), Physics, Nuclear Sciences, Quantum Chromodynamics, Data at University of California, Davis
Bachelor's Degree, Physics, Mathematics, Bachelor's Degree, Physics, Mathematics at Trinity University
English, Bengali, French