Mark J. Bennett
Chicago area, United States
Summary
Mark Bennett is a Senior Data Scientist specializing in AI for the financial services industry, combining six years of current focused experience at NVIDIA advising on accelerated HPC and ML solutions with two decades of quantitative finance and engineering roles. He bridges systems-level parallel computing and business-facing analytics—authoring NVIDIA articles and sample repos on fraud detection, credit risk, NLP, and explainability while presenting on capital markets and quantum simulation. Previously a senior data scientist and quant at Bank of America, he built production classifiers, deep learning pipelines, and pricing/risk models for derivatives and credit risk. An educator and co-author of a Cambridge-published textbook, he teaches graduate analytics courses and translates academic methods into deployable tooling. Based in Chicago, he’s notable for spanning low-level performance optimization to explainable AI in finance—unlocking revenue by solving problems from compiler-like system drivers up through applied business models.
7 years of coding experience
Github Skills (12)
numba9
rapids8
python8
ai8
dask8
gpu8
cudf7
cuda1
quantitative1
machine-learning1
data-science1
java1
Programming languages (1)
Github contributions (4)
MarkJosephBennett/S32407
Mar 2021 - Mar 2021
Contributions:42 pushes, 1 branch in 3 days
MarkJosephBennett/fsi-samples
Apr 2021 - May 2021
A collection of open-source GPU accelerated Python tools and examples for quantitative analyst tasks and leverages RAPIDS AI project, Numba, cuDF, and Dask.
Contributions:12 pushes, 1 branch in 1 month
cudaanalystpythoncudfleverages
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