Heidi Perry is a Principal Engineer in Data Science with 11 years of experience applying advanced quantitative methods and software engineering to real-world systems, currently leading algorithm development and mentoring teams at Hussmann. She built a CARB-approved refrigerant leak early-detection algorithm and the core analytics for a cloud platform deployed in 200+ grocery stores, demonstrating a rare blend of production-grade ML, embedded-systems signal processing, and domain expertise in refrigeration and energy savings. Proficient in Python, C++, R, and DVC-backed workflows, she translates high-frequency time-series and power-signature data into actionable diagnostics and measurable cost and emission reductions. Her background spans academia (PhD in Chemical Physics) to startups and program management, enabling rigorous modeling alongside practical deployment and reporting for stakeholders. Passionate about social good, she has taught data science and chemistry, developed curriculum and open course materials, and consistently emphasizes reproducible, impact-driven analytics.
11 years of coding experience
15 years of employment as a software developer
Bachelor's Degree Chemistry (with Departmental Distinction) and Mathematics, Bachelor's Degree Chemistry (with Departmental Distinction) and Mathematics at University of Washington
Coursera
Ph.D. Chemical Physics, Ph.D. Chemical Physics at Columbia University
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