Requirements

Operating system

Windows

Our requirements

• 4+ years of experience as a Data Scientist or ML Engineer, with hands-on experience applying machine learning and statistical techniques to real-world problems.
• Education – Bachelor’s, Master’s or PhD degree in a relevant field, e.g. data science, computer science, mathematics, statistics, econometrics, physics, engineering or similar.
• Strong theoretical and practical knowledge in statistical modeling, causal inference and experimental design
• Strong practical knowledge of Machine Learning, and ability to build models that can be used in large scale production environments.
• Solid experience with Python 3 and its data science stack (e.g. pandas, numpy, scikit-learn, statsmodels, etc.)
• Experience designing and interpreting A/B tests and other controlled experiments.
• Proven track record of delivering production-level code and working in version-controlled, peer-reviewed codebases.
• Ability to write clean, well-documented, and testable code.
• Strong communication skills and ability to work cross-functionally in a fast-paced environment.
• Experience in collaborative environments and teamwork-oriented culture.

Optional

• Experience with Bayesian methods and probabilistic programming libraries such as PyMC, TensorFlow Probability, or NumPyro.
• Experience with ML Ops practices and tools, such as MLFlow, Airflow, or Databricks.
• Experience in deploying ML models to production environments and supporting their lifecycle.
• Passion for gaming or experience in the mobile gaming industry.

Your responsibilities

• Partner with our product and business teams to design, build and deploy advanced data science solutions.
• Take ownership of full DS/ML project cycles — from ideation and stakeholder alignment, to implementation and ongoing maintenance.
• Design, build, and deploy robust statistical and machine learning models that help us understand and predict player behavior, optimize game features, and personalize user experiences.
• Design methodology, build tools and promote data driven culture for experimentation, including A/B tests, to guide product development and decision-making.
• Build forecasting models to support product, marketing and finance strategy with reliable forward-looking insights.
• Communicate your findings clearly and effectively across both technical and non-technical audiences, using appropriate visualizations and business context.
• Advocate for data science best practices across the organization, contributing to code reviews, mentoring, and the evolution of internal DS standards and tools.
• Collaborate well across the data team to ensure scalable deployment and monitoring of models in production.