Summary
Spencer Bertsch is a Ph.D. research assistant combining nine years of experience across physics, engineering, and data science to build ML pipelines and analytic systems for finance and operations research. He has applied quantitative research and data strategy at EquiLend and Loomis Sayles, and teaches applied machine learning and data analytics at Dartmouth, bridging theory and production. His training spans Dartmouth, MIT (PhD exchange), and Skidmore, with hands-on expertise in statistical modeling, deep learning, time-series and production data architectures. Spencer’s background in mechanical prototyping and competitive ski racing reflects a practical, systems-oriented problem solver who values iterative design and performance under pressure. He’s particularly interested in turning complex financial data into actionable models and governance-ready pipelines that support investment strategy. As a lifelong learner, he blends academic rigor with product-focused engineering to move research into real-world impact.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning Operations Research, Doctor of Philosophy - PhD Machine Learning Operations Research at Dartmouth College
Bachelor of Engineering (BE) Data Science and Applied Mathematics, Bachelor of Engineering (BE) Data Science and Applied Mathematics at Thayer School of Engineering at Dartmouth
Physics Major Mathematics Minor, Physics Major Mathematics Minor at Skidmore College
Maumee Valley Country Day School
IvyPlus PhD Exchange Scholar Artificial Intelligence + Decision Systems, IvyPlus PhD Exchange Scholar Artificial Intelligence + Decision Systems at Massachusetts Institute of Technology
matlab, python, c++, r