Jan Kislinger

Jan Kislinger

Data Scientist · ML Engineer · Recommender Systems Researcher

About Me

I built my career on mathematics and statistics, first applying them in data science and now in ML engineering. At Sky, I design production systems while working closely with teams that develop and evaluate models. I’m also pursuing a PhD in recommender systems at Czech Technical University, where I bring together mathematics, computer science, and user research to study whole-page recommendations.

Resume

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Experience Highlights

  • 📅 Senior MLE @ Sky (Mar 2026 – Present): Designing end-to-end production ML systems in close collaboration with data science teams.
  • 📅 Data Science Manager @ Sky (Apr 2024 – Feb 2026): Led a team of 4 building personalized sports recommendation pipelines.
  • 📅 Data Scientist @ Sky (Jun 2023 – Mar 2024): Localized and deployed recommender models across international markets.
  • 📅 Tech Lead @ Showmax (May 2020 – May 2023): Architected end-to-end ML pipelines (Airflow, Redis) for content personalization.
  • 📅 Data Science Lead @ Oddin.gg (Jul 2018 – Apr 2020): Developed real-time e-sports ML models via gRPC servers.

Education & Skills

  • 🎓 PhD Informatics @ Czech Technical University (2024 – Present)
  • 🎓 MA Probability, Statistics & Optimization @ Charles University (2017)
  • 🎓 BA Financial Mathematics @ Charles University (2014)

Technical Stack

Python TensorFlow PyTorch Polars Rust SQL Flask

Latest Blog Posts

Forged in Rust, Spoken in Python

November 26, 2025

Python makes it easy to build and explore ideas, but performance-critical work often runs in native code. I found that Rust, PyO3, and Maturin made it practical to bring that speed into my whole-page recommender.

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Bringing the Carousel Experience to Recommender Systems

August 7, 2025

If you've ever opened your favorite app—whether for videos, music, shopping, or beyond—you’ve likely seen multiple rows—or "carousels"—of recommendations: “Keep Watching,” “Just for You,” “Hot Right Now,” and so on. Yet, much of the academic research out there still focuses on a single ranked list—a single column of suggestions.

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