Principal Data Scientist

Overview

As Principal Data Scientist, you will drive high-impact, end-to-end data initiatives to boost player engagement and business outcomes. You’ll lead modeling and experimentation across complex systems, collaborating with game teams to translate analytics into product actions. You’ll shape engineering standards, deploy scalable ML solutions, and mentor a growing data science craft. This role offers meaningful impact at a global gaming leader, with opportunities to influence top titles and strategies.

Responsibilities

• Lead end-to-end data science initiatives from framing to impact assessment.
• Build and deploy ML and reinforcement learning solutions to improve engagement, retention, monetization, and operations.
• Define and manage modeling frameworks for causal inference, uplift modeling, sequential decisioning, bandits, RL, and forecasting.
• Partner with game teams to define success metrics and decision frameworks, turning analytics into actionable actions.
• Set engineering standards for model development, validation, uncertainty, reproducibility, and bias checks.
• Drive scalable experimentation with A/B tests and multi-armed bandits, including power analysis and online-offline alignment.
• Collaborate with Data Engineering, MLOps, and Game Tech to ensure reliable data foundations and deployment paths.
• Develop internal data products (e.g., AB-test calculators, decision tools, automated insights) to speed decision-making.
• Provide technical leadership through code reviews, mentoring, and coaching across the org.
• Act as a data-driven advisor to senior leadership to inform critical business decisions.

Key requirements

• 5+ years of professional data science experience.
• Delivered at least 3 data or ML products from problem definition to production and monitoring.
• Proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.
• Hands-on software engineering, MLOps, and scale deployments of ML models.
• SQL, Python; familiarity with big data (Kafka, Spark) and cloud platforms (GCP, AWS, Azure).
• Industry knowledge in gaming or digital entertainment is a plus.
• leadership and mentoring
• stakeholder communication
• collaboration across cross-functional teams
• clustering
• predictive modeling
• reinforcement learning

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