Arindam Paul
Lead Data Scientist (ML Research Team)
Malden, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolArindam Paul is a Lead Data Scientist with a decade of experience applying machine learning research to production problems, currently heading ML Research efforts at American Family Insurance. He holds a Ph.D. in Applied Machine Learning from Northwestern and has a track record of turning advanced models—ranging from LSTMs and attention networks to generative multimodal systems—into business value across claims routing, telematics-based insurance, and financial forecasting. At AmFam he leads generative-AI initiatives for conversational intelligence and audio summarization, runs a seminar series on fairness and ethics, and co-authors research with academic partners on topics like forest fire detection and AI fairness. Known for blending rigorous academic methods with pragmatic engineering, he’s built ensemble and physics-augmented models that achieved order-of-magnitude gains in design search and highly accurate predictive systems in materials and solar cell research. Based in Malden, MA, he mentors junior data scientists and bridges research, product, and ethical considerations to deploy reliable, auditable ML solutions.
10 years of coding experience
12 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Northwestern University
English, Bengali, Hindi, Urdu
Github Skills (29)
machine-learning9
incremental-learning8
blackbox6
streaming6
transparency6
streaming-data6
python6
xai6
interpretable-machine-learning6
stream-processing5
supervised-learning5
predictive-modeling5
differential-privacy5
iml5
statistics5
Programming languages (4)
Github contributions (5)
NU-CUCIS/microstructure-optimization-sampling
Dec 2015 - Mar 2022
Contributions:25 pushes, 1 branch in 6 years 3 months
NU-CUCIS/ml-iter-additive
Sep 2018 - Mar 2021
An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process
Contributions:78 commits, 48 pushes, 1 branch in 2 years 6 months
machine-learningadditive-manufacturingiterative-methodspredicting-temperature-profilesbagged-forests
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