Playtika

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

Required skills

As a Data Operations Engineer you will be responsible for managing applications and data infrastructure in production environments fully hosted in private clouds and on-premise data centers, taking overall responsibility for the quality of the data services that the company provides.

The technology stack is mainly: Linux, Docker, Kubernetes, Ansible, Jenkins, TeamCity, Aerospike, MariaDB, PostgreSQL, Vertica, Hadoop ecosystem, SingleStore, ELK stack, Kafka, Git.

Infrastructure: private clouds, on-premise data centers.

Responsibilities:

• Deep understanding of the company applications and IT infrastructure;
• Support existing data infrastructure;
• Participation in the creation of new infrastructure parts, documentation development, customer onboarding;
• Being a focal point for R&D teams;
• Participate in investigation process (in case of any issues/incident for specific R&D team);
• Providing solutions and support for critical IT environment.

Requirements:

• At least 3 years of practical experience in Linux administration;
• Experience with containerization and container orchestration systems (e.g. Kubernetes);
• Understanding of TCP/IP network stack;
• Good troubleshooting skills and basic application debugging;
• Understanding/experience with message queue systems (e.g. Apache Kafka);
• Acquaintance with MySQL, PostgreSQL, Aerospike (or other NoSQL solutions).

Nice to have:

• Experience with VCS (preferably Git);
• Experience with Hadoop ecosystem (HDFS, YARN, Spark, Hive);
• Experience with CI/CD systems such as Jenkins, TeamCity;
• Experience in managing systems with configuration management (Ansible).