Available to hire
I’m Himanshu Saini, a data scientist with 4+ years of computational research and applied ML experience. I’ve built and shipped a real-time computer-vision system that runs 10–15 FPS on-device and trained high-recall models for semiconductor yield and credit-risk prediction.
My strengths include rare-event classification and cost-sensitive evaluation. I’ve delivered results such as ~75% recall in manufacturing yield and ~77% recall in credit risk, and I excel at turning data into robust, deployable solutions across academia and industry.
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
Language
English
Fluent
Work Experience
Computational Scientist at McMaster University
December 1, 2023 - October 2, 2025Built WloopPHI, a Python tool integrated with WIEN2k for Berry-phase and Wilson-loop analysis of Weyl semimetals, reducing analysis time by ~60%. Conducted DFT studies to evaluate thermodynamic stability and topological phases; contributed to peer-reviewed publication. Developed a Python module for phase-diagram and convex-hull visualization to support materials screening.
Junior Research Fellow at Research Institute, SRM University Chennai, India
July 1, 2019 - October 2, 2025Automated high-throughput DFT workflows for 2D materials screening; parameter sweeps and batch submissions; modeled MoS2/2D heterojunctions and g-C3N4 quantum dots; supported solar-cell and photodetector studies.
Education
MASc, Materials Engineering at McMaster University
September 1, 2019 - June 1, 2023MTech, Nanotechnology at SRM Institute of Science & Technology
July 1, 2014 - June 1, 2016Qualifications
NSERC Graduate/Research Scholarship
September 1, 2019 - October 2, 2025Junior Research Fellowship, Ministry of New & Renewable Energy
July 1, 2016 - October 2, 2025Industry Experience
Software & Internet, Manufacturing, Media & Entertainment, Education, Professional Services, Financial Services
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Intermediate
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