· Build and own the company’s data science and analytics function from the ground up.
· Partner with executive leadership, product, live operations, marketing, finance, and technology teams to turn business questions into clear insights, recommendations, and action plans.
· Develop the core analytics roadmap across player behavior, retention, engagement, monetization, game health, content performance, live operations, user segmentation, forecasting, and business performance.
· Personally perform hands-on analysis, including SQL querying, dashboarding, data modeling, KPI development, reporting, statistical analysis, and business case development.
· Improve the quality, consistency, and reliability of company metrics, reporting, and data definitions.
· Identify gaps in data collection, instrumentation, data architecture, reporting, and operational visibility.
· Partner with engineering and data stakeholders to improve data pipelines, data quality, data governance, and access to trusted datasets.
· Design dashboards and executive reporting that help leaders understand what is happening, why it is happening, and what should be done next.
· Lead analysis on player lifecycle, cohort behavior, monetization trends, feature performance, content performance, game economy, and live-service operations.
· Support experimentation, A/B testing, measurement frameworks, and decision-ready analysis for product and business initiatives.
· Implement practical AI-driven solutions to accelerate analytics workflows, automate repetitive reporting, improve insight generation, and support better business decisions.
· Explore and deploy AI agents, copilots, workflow automations, and other tools that improve productivity across analytics, operations, product, and leadership reporting.
· Establish best practices for responsible AI use, data privacy, analytical rigor, documentation, and repeatable workflows.
· Over time, hire, coach, and lead a high-performing data science, analytics, and/or BI team.