James Fiedler is a Staff Machine Learning Engineer with 12 years of applied experience building statistical and ML solutions across finance, research, and consumer platforms. He holds a Ph.D. in Mathematics and has progressed from biostatistics and research roles to senior data science leadership at Capital One and hands-on ML engineering at Indeed, combining rigorous statistical thinking with production-grade Python engineering. James blends Bayesian and frequentist approaches with practical machine learning techniques—boosting, stacking, and deep learning—and builds reusable data-science tooling and visual metaphors to make complex results actionable. An active contributor to causal inference tooling (notably adding notebooks to an implementation of Hernán & Robins’ Causal Inference: What If), he brings both theoretical depth and reproducible code practices to teams. Based in Austin, he is comfortable learning new languages and tools to solve problems and favors clear, visual communication of analytic insights.
12 years of coding experience
11 years of employment as a software developer
Ph.D., Mathematics, Ph.D., Mathematics at Iowa State University
B.S., Physics, B.S., Physics at Creighton University
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
Role in this project:
Data Scientist
Contributions:28 commits, 2 PRs, 22 pushes in 3 years 11 months
Contributions summary:James added and modified several Jupyter notebooks for the causal inference python code repository. The commits included adding notebooks for different chapters and performing calculations. The user has also fixed a typo and made other small adjustements. The changes suggest this user is working on the implementation of causal inference methods and analyses.
Contributions:10 commits, 1 PR, 7 pushes in 2 years 9 months
kernelpythonjupyter-notebookjupyterlabstata
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James Fiedler - Staff Machine Learning Engineer at Indeed