Data Scientist

Job Responsibilities

• Build and productionize ML models that drive product and marketing decisions — player segmentation, player behavior prediction, and growth modeling.
• Design personalization and optimization systems that decide what each player sees, and the decision logic behind them — not just the prediction.
• Own your pipelines in production. Build and maintain ETL and model workflows on our orchestration stack, and keep them healthy with monitoring, data quality checks and alerting.
• Design and run experiments. Define test groups, choose the right metrics, evaluate results honestly, and drive the decision together with the product and marketing teams.
• Run exploratory analysis on player and monetization data to find scalable ways to improve engagement, retention and revenue — and turn findings into a concrete, shippable action.

Job Requirements

• Degree in Engineering, Statistics or a related quantitative field.
• 1+ years of hands-on data science or Python development experience, with code that reached production
• Strong Python, with clean, reviewable, maintainable code. Comfortable with Git and code review.
• Strong SQL and real experience with a columnar cloud warehouse — BigQuery preferred.
• Practical ML depth in supervised learning on tabular data: gradient boosting, classification and regression, proper validation, class imbalance, feature engineering and model interpretability.
• Experience in data engineering and familiarity with tools such as GCP, Apache Airflow, Docker, Flask.
• Statistics and experimentation fundamentals: hypothesis testing, confidence intervals, sample size.
• Effective use of AI tools in your daily workflow, with the judgment to review and validate what they produce rather than shipping it blindly.