Piyush Gautam
Product Manager Enterprise at Intertec Systems
India
Summary
👤
Senior🎓
Top SchoolPiyush Gautam is a product-focused technologist with 9 years of experience building AI-driven products and scalable backend systems across global markets including India, GCC and Africa. He transitioned from hands-on ML and SDE roles at Samsung, Gap Inc., and research labs—where he delivered measurable engagement and cost reductions—to product leadership after an MBA at IIM Ahmedabad. At Samsung he led end-to-end AI pipelines and generative AI adoption, while his open-source contributions to the ArviZ Bayesian analysis library demonstrate deep competence in statistical tooling and scientific-grade debugging. Currently owning enterprise product strategy and GTM at Intertec Systems, he blends technical credibility with customer-centric roadmapping and go-to-market execution. Notably, he earned a Samsung SPOT award (top 1%) and a PPO from Adobe after driving an AI-first mobile retention initiative, signaling both technical impact and business acumen.
9 years of coding experience
4 years of employment as a software developer
Master of Business Administration - MBA, Business, Master of Business Administration - MBA, Business at Indian Institute of Management AhmedabadSenior Secondary, Science, Senior Secondary, Science at GSSS PanjgainBachelor of Technology - BTech, Electronics and Communications Engineering, Bachelor of Technology - BTech, Electronics and Communications Engineering at National Institute of Technology Hamirpur-Alumni
Github Skills (11)
bayesian-statistics10
xray10
bayesian10
bayesian-data-analysis10
python10
bayesian-inference10
data-science10
numpy10
xa10
scipy9
jupyter-notebook8
Programming languages (1)
Github contributions (5)
arviz-devs/arviz
Jan 2020 - Jan 2022
Exploratory analysis of Bayesian models with Python
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:9 reviews, 28 commits, 28 PRs in 2 years
Contributions summary:Piyush primarily contributed to the ArviZ library by addressing specific issues and enhancing its functionality. Their work includes clarifying the usage of the `plot_joint` function, integrating the `point_estimate` parameter with `rcParams`, and fixing bugs related to `hpd` calculations. They also contributed to the addition of the `group` argument in the `plot_pair` function and improvements to the HPD calculation, including multimodal distributions. They also helped add JSON converter and add test for it.
pythonexploratorybayesian-inferencemachine-learningbayesian-models
percygautam/arviz
Jan 2020 - Sep 2020
Exploratory analysis of Bayesian models with Python
Contributions:349 pushes, 47 branches in 8 months
pythonexploratorybayesian-inferencemachine-learningbayesian-models
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