Nikhil Joglekar
Technical Program Manager at Stripe
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNikhil Joglekar is a Technical Program Manager with 12 years of experience orchestrating product launches and cross-functional delivery across cloud and payments platforms, currently driving programs at Stripe from Seattle. He previously led launch initiatives for Google Compute Engine and managed product work across Azure’s algorithms, data science, and diagnostics teams at Microsoft, blending deep technical understanding with program-level execution. With dual technical and business degrees from UT Austin, he navigates complex trade-offs between engineering, data science, and stakeholder goals. He also contributes to open-source recommender tooling—publishing a hands-on SAR algorithm notebook that showcases practical experience with recommendation metrics and end-to-end model workflows. Known for translating research-grade ideas into production-ready programs, he combines a data-driven mindset with a knack for shipping scalable cloud features.
12 years of coding experience
7 years of employment as a software developer
BS, Electrical and Computer Engineering Honors, BS, Electrical and Computer Engineering Honors at The University of Texas at AustinBBA, Finance, BBA, Finance at The University of Texas at Austin - Red McCombs School of Business
Github Skills (7)
pandas10
recommendation-system10
python10
data-science9
evaluation9
machine-learning9
jupyter-notebook9
Programming languages (7)
Github contributions (5)
recommenders-team/recommenders
Oct 2018 - Mar 2019
Best Practices on Recommendation Systems
Role in this project:
Data Scientist
Data ScientistContributions:115 commits, 7 PRs, 13 pushes in 5 months
Contributions summary:Nikhil contributed a quick start notebook for running the SAR (Smart Adaptive Recommendation) algorithm on the MovieLens dataset using Python. The notebook demonstrates the end-to-end process, including data loading, splitting, model training, and evaluation using ranking metrics. The user focused on implementing the SAR algorithm and evaluating its performance using ranking metrics.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
microsoft/vs-diag-samples
Apr 2016 - Sep 2017
Contributions:21 commits, 4 PRs, 15 pushes in 1 year 5 months
diagnosticspostsblog-postsvisual-studio
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