Hiren Kelaiya - Senior Data Scientist

Hiren Kelaiya

Senior Data Scientist

Bengaluru, Karnataka, India

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Summary

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Senior

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Top School

Hiren Kelaiya is a Senior Data Scientist based in Bengaluru with a decade of experience building and deploying scalable ML and LLM-driven solutions across enterprise and product contexts. He has a strong academic foundation—a Gold Medalist M.Sc. in Physics and a B.E. in E.C.—and blends scientific rigor with practical engineering to deliver predictive maintenance, anomaly detection, and generative AI systems. At Happiest Minds he led LLM initiatives that cut delivery timelines by 25% and drove a 30% productivity uplift through team leadership and ML best practices. He is fluent in cloud deployments (AWS, Azure, GCP) and modern ML stacks (PyTorch, TensorFlow, scikit-learn), and repeatedly bridges product, engineering, and business needs from pre-sales scoping to post-deployment support. Known for quick adoption of emerging AI tech, he also explores human-centered aspects of intelligent systems—bringing insights from physics and psychology into model design. Practical, pragmatic, and client-facing, he excels at turning complex research-grade ideas into reliable production services.

9 years of coding experience

5 years of employment as a software developer

Gujarat Technological University

Data Science Transition, Data Science, Data Science Transition, Data Science at GreyAtom

12th Standard, Science, PR 94.28, 12th Standard, Science, PR 94.28 at Sanidhya Science School, Rajkot

Master of Science (M.Sc), Physics, CGPA: 9.66 (Gold Medalist), Master of Science (M.Sc), Physics, CGPA: 9.66 (Gold Medalist) at CHAROTAR UNIVERSITY OF SCIENCE AND TECHNOLOGY

English, Hindi, Gujarati

Github Skills (7)

flask-application9

medicine9

machine-learning6

artificial-intelligence6

analytics5

data-science5

mlops4

Programming languages (1)

Jupyter Notebook

Github contributions (5)

hirenhk15/risk-models-for-medical-prognosis

Jun 2020 - Mar 2021

An End-to-end Machine Learning System with User Interface for predicting the 10-year risk of death of individuals from the NHANES I epidemiology dataset. This project leverages risk models (a.k.a. prognostic models) built using machine learning to perform predictions given the patient's medical profile.

Contributions:1 release, 80 commits, 15 pushes in 9 months

analyticsartificial-intelligencedatadata-scienceflask-application

hirenhk15/ga-learner-dst-repo

Sep 2020 - Jun 2021

Contributions:44 pushes, 1 branch in 9 months

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