Kate Pearce
Group Product Manager at Solace
Denver, Colorado, United States
Summary
👤
Senior🎓
Top SchoolKate Pearce is a Group Product Manager with 8 years of experience applying engineering and data-driven approaches to improve healthcare coordination and patient empowerment. She blends hands-on technical chops (including contributions to TensorFlow Probability implementing Weibull distributions and Brownian motion examples) with product leadership, bringing Engineering, Design, and Clinical teams together to translate complex public health requirements into scalable digital solutions. Her background spans frontline COVID-19 and behavioral health systems, state-level integrations, and mobile health apps, giving her a rare mix of clinical empathy and technical fluency. Fluent in both research-grade data work from MIT and production delivery in government and startup settings, she excels at turning ambiguous policy goals into maintainable architectures and measurable outcomes. Based in Denver, she’s equally comfortable mentoring teams, teaching math to the next generation, and writing the scripts that make multimodal health data actionable.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science and Molecular Biology, Minor: Mathematics, Bachelor of Science - BS, Computer Science and Molecular Biology, Minor: Mathematics at Massachusetts Institute of Technology
English, Spanish
Github Skills (8)
statistics10
bayesian-methods10
machine-learning10
probabilistic-programming10
tensorflow10
python10
data-science10
deep-learning8
Programming languages (3)
Github contributions (5)
tensorflow/probability
Jun 2020 - Jan 2021
Probabilistic reasoning and statistical analysis in TensorFlow
Role in this project:
Data Scientist
Data ScientistContributions:14 commits in 7 months
Contributions summary:Kate primarily contributed to the implementation and testing of the Weibull distribution within the TensorFlow Probability library, demonstrating proficiency in probabilistic modeling. Their work involved adding new functionality, including CDF, PDF, and sample generation. Furthermore, they added examples and tests for a Brownian motion model and added an ASVI surrogate posterior. These contributions directly enhance the library's capabilities for statistical analysis and probabilistic reasoning.
statisticspythonprobabilistic-reasoningdata-sciencedeep-learning
kateslin/kateslin.github.io
Feb 2018 - Nov 2024
Contributions:51 pushes, 1 branch in 6 years 10 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.