Ivan Lisenkov is a Senior Research Data Scientist with 13 years blending theoretical physics, numerical simulation and production-grade machine learning to accelerate experimental R&D and product analytics. He designs reproducible pipelines and custom toolboxes in Python (SciPy, scikit-learn, scikit-rf) to automate data acquisition, COMSOL simulations and visualization, reducing testing and computation times by an order of magnitude on past projects. At Roku and prior roles he has taken models from experiment planning to deployment, led ML pipeline teams, and applied Bayesian methods to personalize user experiences. A published researcher with 22 peer-reviewed papers and 25 conference talks, he routinely serves as reviewer/editor and has led DARPA-funded signal-processing work and government-contracted R&D. Based in Natick, MA, he champions reproducible research via git, Docker and CI automation, bringing a rare mix of deep math, RF simulation experience, and production engineering.
13 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS, Applied Mathematics and Physics, Bachelor of Science - BS, Applied Mathematics and Physics at Moscow Institute of Physics and Technology (State University) (MIPT)
Propose optimal business strategy for a volatile real-estate market. Used machine learning algorithms to forecast listing and rental prices in local markets based on openly available market data
Contributions:56 commits, 2 PRs, 40 pushes in 7 days
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