Data Science Expert

Join us at Playtika (NASDAQ: PLTK), where we're driven by the belief life needs play. We’re on a mission to deliver infinite ways to play using cutting‑edge technologies like AI and machine learning to craft immersive experiences that connect, inspire and entertain millions of players worldwide.

From our start as a small mobile games company founded in Israel to our current position as a publicly traded company and industry leader, we continue to be a dominant force in interactive entertainment. With a diverse portfolio of award‑winning, category‑leading Casual and Social Casino‑themed games, including nine of the top 100 highest‑grossing mobile games in the US, we're setting the standard for excellence.

Our success story is co‑authored by a dynamic team of storytellers, strategists, creators and data scientists who thrive on innovation. We are home of the best, advancing an inclusive culture that embraces our core values and reflects our agile DNA.

With a strong financial foundation, disciplined operations, unwavering player‑focused approach and relentless can‑do spirit, we're well‑positioned for sustained growth. If you're ready to join the driving force behind the evolution of interactive entertainment, we invite you to come play with us.

Playtika is looking for a Data Scientist Expert – Production ML to join the Data & AI department.

In this position you will join a multidisciplinary team focused on personalization using Reinforcement Learning methods. Over the last few years, we have built a real‑time recommendation engine based on Bayesian Multi‑Armed Bandits - including our open‑source PyBandits library - serving millions of players across multiple studios and use‑cases.

This is an applied research role, with impact measured in production. Your primary contribution will be advancing the mathematical and statistical foundations of our existing solutions: identifying limitations, deriving principled improvements, and owning those improvements all the way through to production. You will not be handed research problems from above – you will find them yourself, inside systems that are already running at scale.

Scaling here is fundamentally a mathematical problem: identifying better statistical methods, proving they hold under real‑world constraints, and knowing exactly how far they deviate from the ideal. That's what sets this role apart.

• Identify mathematical and statistical limitations in existing ML solutions and deliver provably correct, more efficient alternatives
• Validate the correctness of scaled solutions - diagnosing where distributed execution or approximation invalidates prototype‑level results, and redesigning accordingly
• Own the full lifecycle of your improvements: from research and prototyping through production deployment and impact measurement
• Drive the evolution and scalability of our personalization and recommendation systems to meet future business and technical requirements
• Contribute to end‑to‑end data‑driven research including problem definition, data collection, model development, evaluation, and deployment
• Collaborate with cross‑functional teams (engineers, BI, Product) to deploy solutions quickly and effectively
• Mentor Data Scientists and ML Engineers, and serve as a technical reference on statistical and mathematical rigor across the organization

• M.Sc or PhD in Computer Science, Mathematics, Statistics, Engineering or a related field
• 5+ years of relevant working experience, including significant production ML ownership
• Demonstrated experience taking statistically sophisticated models from prototype to production and operating them successfully under real‑world business constraints.
• Experience with neural network architectures, including the ability to diagnose, interpret and debug their behavior in probabilistic or decision‑making contexts (e.g., Bayesian Neural Networks, Neural Linear models)
• Strong theoretical foundation in at least one of:
  Multi‑Armed Bandits, Bayesian methods, online learning, recommendation systems, or related decision‑making frameworks
• Strong understanding of experimentation, causal inference and statistical evaluation of online…