Principal Data Scientist

Role Overview

• Lead high-impact data science initiatives end-to-end, including problem framing, methodology selection, experiment development, implementation partnership, and impact measurement.
• Build and deliver machine learning and reinforcement learning solutions to improve player engagement, retention, monetization, and operational outcomes.
• Lead the modeling framework for complex systems, guaranteeing comprehensive evaluation and monitoring of causal inference, uplift modeling, sequential decisioning, bandits/reinforcement learning, and forecasting.
• Partner with game teams to define success metrics, guardrails, and decision frameworks, translating analytical results into actionable product and operational actions.
• Define and uphold engineering standards and guidelines for model development, including validation, uncertainty, reproducibility, and bias/quality checks.
• Drive scalable experimentation with A/B and Multi-armed bandit testing frameworks, power analysis, variance reduction, and online-offline alignment.
• Work together with Data Engineering, MLOps, and Game Tech teams to guarantee dependable data foundations, feature accessibility, and model deployment pathways.
• Build internal data products to improve the speed and quality of decision-making, such as AB-test calculators, decision tools, and automated insights.
• Provide technical leadership through building and code reviews, mentoring, and coaching, improving the standard of data science craft across the organization.
• Serve as a reliable collaborator throughout the organization, promoting data-informed decision-making and enabling business units to embrace data products.
• Translate complex analytical insights into actionable recommendations, presenting them to senior leadership to inform critical business decisions and encourage collaborators.

Requirements

• PhD or MSc in Data Science, Computer Science, Statistics, Physics, Mathematics, or a related quantitative field, or equivalent experience in practice.
• 5+ years of professional data science experience.
• At least 3 data or ML products from problem definition to production deployment and monitoring.
• Proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.
• Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.
• Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure).

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Kafka
• Python
• Spark
• SQL

Benefits

• Health insurance
• 401(k) matching
• Paid time off
• Flexible work arrangements
• Professional development opportunities