Jordan Melendez
Data Scientist
Columbus, Ohio, United States
Summary
👤
Senior🎓
Top SchoolJordan Melendez is a data scientist with a decade of experience blending Bayesian statistical modeling and applied machine learning across academia and industry. Currently at Root Insurance and a visiting fellow at Ohio State, he leverages his Ph.D. in nuclear physics to model complex physical and risk systems using Python and probabilistic programming. He contributed multivariate distributions (MatrixNormal, KroneckerNormal) to the prominent PyMC project, extending Bayesian tooling for real-world inference. His background includes research positions at LLNL and CERN and an internship at DataRobot, giving him rare fluency in both high-performance scientific computation and production ML workflows. Colleagues rely on him to translate mathematically rigorous methods into robust, test-covered implementations that drive insight and decision-making.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.), Physics, Bachelor of Science (B.S.), Physics at Taylor UniversityDoctor of Philosophy (Ph.D.), Nuclear Physics, Doctor of Philosophy (Ph.D.), Nuclear Physics at The Ohio State University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
statistics10
mcmc10
pymc10
bayesian10
probabilistic-programming10
python10
bayesian-inference10
statistic10
numpy9
theano9
Programming languages (4)
Github contributions (5)
pymc-devs/pymc
Nov 2017 - Feb 2018
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist
Data ScientistContributions:15 commits, 2 PRs, 33 comments in 2 months
Contributions summary:Jordan primarily contributed to the implementation of the `MatrixNormal`, `KroneckerNormal` distributions and related functions within the PyMC3 library. This involved writing code for these multivariate distributions, including defining their mathematical properties, random sampling methods, and log probability calculations. Furthermore, the user added unit tests to verify their correctness. This work extended the library's capabilities in Bayesian modeling and probabilistic programming.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
buqeye/nuclear-matter-convergence
Sep 2019 - May 2020
Contributions:113 commits in 8 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.