Join Playtika as a Data Scientist Expert specializing in Production ML, leveraging Reinforcement Learning for personalization. Drive the evolution of real-time recommendation systems, applying advanced statistical methods to deliver impactful, scalable solutions for millions of mobile game players worldwide.
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.
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
Qualifications
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, including drafting job descriptions, communication, response to applications, assessments and tests, as further described in our Candidates Privacy Notice.
By applying to this job, you acknowledge that you have read and understood our Candidates Privacy Notice, including with respect to the use of AI tools, and consent to the processing of your information as described therein.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you feel the above describes you perfectly - Apply now!
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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; Experience with neural network architectures; Strong theoretical foundation in Multi-Armed Bandits, Bayesian methods, online learning, or recommendation systems; Strong understanding of experimentation, causal inference and statistical evaluation of online decision systems; Strong hands-on Python engineering skills; Proficiency in SQL; Clear written and verbal communication.
