Requirements

Expected technologies

Python

SQL

Our requirements

• 5+ years of experience in Data Science or Machine Learning, ideally in product-oriented and highly data-driven environments.
• Proven track record of end-to-end ownership of ML or analytics projects - from concept to production.
• Strong theoretical and practical knowledge in statistics, machine learning, and experimental design.
• Deep hands-on experience with Python (pandas, numpy, scikit-learn, TensorFlow/PyTorch, statsmodels).
• Proficiency in SQL and experience with PySpark or other big data tools.
• Familiarity with Cloud Infrastructure, ML Ops and deployment practices (for example Databricks, MLFlow, Github Actions).
• Ability to communicate complex insights clearly to non-technical audiences.
• A proactive, ownership-driven mindset and ability to thrive in a dynamic, fast-paced environment.

Optional

• Extensive practical experience in Product Data Science, with a strong focus on experimentation methodologies, causal inference and data-driven product decision-making.
• Experience building scalable, production-level data science solutions using best practices from software engineering.
• Experience with Bayesian modeling and probabilistic programming (PyMC, TFP, NumPyro).
• Passion for mobile games, understanding player behavior, and modeling game economy.

Your responsibilities

• Lead the design, development, and deployment of advanced data science solutions that drive measurable business impact.
• Build and productionize machine learning models for improving player engagement and monetization.
• Design methodology and develop tooling for the experimentation at scale to support data-driven decision-making.
• Implement and promote state-of-the-art methods in causal inference, statistical modelling and model validation.
• Collaborate closely with cross-functional teams (including Product, Marketing, and Engineering) to deliver end-to-end solutions.
• Shape technical standards and help build scalable internal tools.
• Ensure high-quality, maintainable, and well-documented code in a collaborative environment.
• Champion best practices in Data&AI across the company through professional communication, education, and innovation.