Richard Shadrach
Director, Data Science
Cincinnati, Ohio, United States
Summary
🤩
Rockstar🎓
Top SchoolRichard Shadrach is a Senior Scientific Developer with seven years of industry experience applying advanced mathematics and optimization to real-world data problems at 84.51˚. He combines deep Python scientific-computing expertise (pandas, NumPy, Pyomo, PySpark) and MINLP/Gurobi/IPOPT optimization skills with a Ph.D. in Mathematics, enabling him to translate theoretical methods into production-ready analytics. Richard has contributed to the core pandas library, fixing nuanced data-type and resampling edge cases that improve reliability for downstream data workflows. His background in C++, Java and systems-level projects reflects a broad engineering foundation, while prior academic roles show a track record of teaching, publishing, and outreach. Based in Cincinnati, he brings both rigorous research instincts and practical software delivery experience to complex data-science and optimization challenges.
7 years of coding experience
2 years of employment as a software developer
B.S., Mathematics, Computer Science, 3.74, B.S., Mathematics, Computer Science, 3.74 at Virginia TechDoctor of Philosophy (Ph.D.), Mathematics, Doctor of Philosophy (Ph.D.), Mathematics at Michigan State University
English
Stackoverflow
31reputation
3kreached
2answers
2questions
Github Skills (12)
pandas10
python10
numpy10
data-analysis10
data-science9
data-structure9
debug9
data-structures9
testing8
pyomo6
ipopt6
optimization6
Programming languages (5)
Github contributions (5)
pandas-dev/pandas
Mar 2020 - Jan 2023
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:2731 reviews, 268 commits, 785 PRs in 2 years 10 months
Contributions summary:Richard made contributions to the pandas library focused on improving data analysis and manipulation capabilities. They addressed bugs related to data type consistency when processing boolean data within grouped operations. Additionally, they implemented enhancements for correct handling of edge cases in value counting and date/time resampling operations. The user's work involved modifying core library functions related to data handling, testing, and ensuring the correct data type across pandas.
pythondatalabeled-datamanipulationdataframes
pandas-dev/asv-runner
Dec 2024 - Mar 2025
https://pandas-dev.github.io/asv-runner/
Contributions:62 PRs, 173 pushes, 76 branches in 3 months
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