Your Impact Strategic Leadership (40% of role)

• Uncover hidden patterns and opportunities in our data that drive meaningful business growth
• Develop and execute an advanced data strategy that leverages machine learning for personalization and optimization
• Partner with Product and Marketing leaders to implement data-driven decision frameworks
• Lead and mentor a small team of data scientists, analysts, and a data engineer, fostering a culture of discovery and innovation

Hands-on Analytics & Data Science (60% of role)

• Drive key analyses using our analytics stack (Metabase, PostHog, SQL) to uncover actionable insights about user behavior, game performance, and marketplace dynamics
• Design and scale sophisticated recommendation systems that significantly improve game discovery and user satisfaction
• Develop price and promotion optimization models that maximize both user value and business outcomes
• Implement and maintain financial processing systems and analytics for transaction data
• Implement and evolve A/B testing frameworks that enable both product and marketing teams to rapidly iterate
• Build and maintain dashboards and reporting systems that enable teams to make data-driven decisions

• 5+ years of combined data science and analytics experience, with at least 2 years leading technical teams
• Strong track record implementing machine learning models in production environments
• Experience with recommendation systems, personalization, or similar user-focused data applications
• Background in experimental design and A/B testing frameworks
• Familiarity with data pipeline development and production data infrastructure
• Demonstrated ability to translate complex analyses into business impact
• Excellence in making technical concepts accessible to non-technical stakeholders
• Familiarity with financial data processing and transaction analytics is a plus

• Strong Python proficiency (including pandas, SciPy, and ML libraries like TensorFlow or Implicit)
• Experience with SQL and database technologies (MySQL or similar)
• Familiarity with cloud platforms (Google Cloud preferred, including BigQuery)
• Knowledge of data infrastructure components (Docker, Redis, message queues)
• Experience with data visualization tools and dashboarding
• Understanding of software development practices and production deployment