Frank Fineis
Data Scientist
Chicago, Illinois, United States
Summary
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RockstarFrank Fineis is a Director of Machine Learning Engineering based in Chicago with 11 years of experience building production ML systems across insurance, finance, and industrial analytics. He holds master's degrees in Statistics (Northwestern) and Applied Mathematics (University of Washington) and blends rigorous academic training with hands-on data engineering and modeling expertise. At Clearcover he progressed from senior engineer to director, leading ML teams that move research into reliable, scalable production services. Frank is an active open-source contributor—his work on LightGBM’s Dask integration improved distributed training, evaluation support, and robustness for large-scale ranking tasks. He has a track record of consulting and cross-disciplinary roles (data science, data engineering, risk management) that make him effective at translating domain needs into performant ML pipelines. Colleagues rely on him for pragmatic solutions that balance statistical rigor, operational reliability, and business impact.
11 years of coding experience
Github Skills (10)
lightgbm10
machine-learning10
dask10
python10
gradient-boosting10
distributed-computing10
data-mining9
decision-tree9
testing8
pytest8
Programming languages (10)
Github contributions (5)
lightgbm-org/LightGBM
Jan 2021 - Jun 2021
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:
ML Engineer
ML EngineerContributions:118 reviews, 8 commits, 12 PRs in 5 months
Contributions summary:Frank primarily contributed to the LightGBM Dask integration, enhancing its capabilities for distributed machine learning tasks. Their work involved implementing and refining the `DaskLGBMRanker`, addressing test flakiness, and optimizing data handling within the Dask framework. They also added support for evaluation sets and custom evaluation functions, further improving the model training process and expanding functionality for users.
classificationdecision-treegbmgradient-boostingmachine-learning
NUStatBioinfo/DegNorm
Jun 2018 - Jan 2020
Normalizing RNA degradation in RNA-seq data
Contributions:137 commits, 49 PRs, 142 pushes in 1 year 6 months
rna-seq
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