Ilya Katsov
CTO, Americas Region at Grid Dynamics
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolIlya Katsov is a seasoned technology leader and CTO for the Americas with 13+ years of experience turning AI and robotics research into deployable industrial solutions for Fortune 500 clients. He founded and scaled Physical AI and enterprise AI practices, launching products in smart manufacturing, supply chain, and robotic manipulation while forging strategic partnerships with hyperscalers like Google Cloud and NVIDIA. A hands-on leader who grew multiple practices at Grid Dynamics from scratch to hundreds of engineers, he blends deep technical delivery (from MIMO research at Intel to reinforcement-learning price optimization in his TensorHouse repo) with go-to-market and pre-sales strategy. Author of books on enterprise AI and algorithmic marketing, he advises senior executives on AI strategy and frequently speaks at industry events. Based in the San Francisco Bay Area, he combines academic rigor in computer science and cryptography with practical experience shipping large-scale AI platforms and products.
13 years of coding experience
16 years of employment as a software developer
Master’s Degree Computer Science and Cryptography, Master’s Degree Computer Science and Cryptography at Saint Petersburg State University of Aerospace and Instrumentation
Github Skills (13)
model-building10
pytorch10
machine-learning10
jupyter-notebook10
deep-learning10
python10
modeling10
reinforcement-learning10
model-driven10
model-driven-development10
data-visualisation9
data-visualization9
data-visualizations9
Programming languages (4)
Github contributions (5)
ikatsov/tensor-house
Nov 2017 - Jan 2023
A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 35 commits, 4 PRs in 5 years 2 months
Contributions summary:Ilya's primary contribution appears to be the initial implementation of a price optimization model using Deep Q-Network (DQN) reinforcement learning within a Jupyter Notebook. The code modifications include importing relevant libraries for machine learning, visualization, and interacting with the environment. The core of the work involves defining an environment for price optimization, implementing a DQN algorithm for price schedule optimization and visualizing the model's performance. There are also examples of basic baselines for comparison.
pricingqoptimization-modelsdata-scienceoptimization
ikatsov/tensor-house-data
Nov 2020 - Nov 2022
Contributions:3 commits, 6 pushes, 3 branches in 2 years
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