Description
Ambition Needs a Place to Grow.
At Playtika, we create mobile games enjoyed by millions worldwide.
Combining the scale of a public company (NASDAQ: PLTK) with the speed and ownership of a startup, we are a global leader where innovation, data, and creativity shape the future of entertainment.
Here, your ideas matter, your decisions impact products, and your growth is supported across global studios.
Bring your hunger where it belongs: at the front of the pack.
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.
Responsibilities:
- 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
Requirements:
- 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 decision systems.
- Strong hands-on Python engineering skills, with the ability to design, implement, review, and maintain production-quality code, leveraging modern AI coding assistants (e.g., Claude, Cursor) to improve productivity and code quality
- Proficiency in SQL
- Clear written and verbal communication - able to explain statistical reasoning to non-technical stakeholders
- Ability to operate autonomously and influence decisions across teams without direct authority.
Nice to have
- Experience with probabilistic programming frameworks (e.g. PyMC or Numpyro)
- Familiarity with MLOps tooling (experiment tracking, model serving, monitoring)
- Familiarity with approximate inference methods (variational inference, MCMC)
- Experience with PySpark
- Experience in the Mobile Gaming industry
Subject to applicable laws, we may use artificial intelligence (AI) tools to assist our recruitment teams in the hiring process,
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