A demanding dual-competency role at the intersection of product analytics and data science. Most candidates are strong on one side; we are looking for someone strong on both. You understand player behavior in depth, and you build the models and agents that make those insights actionable at scale. You are fluent in the language of Product Managers and in Python.
Product Analyst side
Analyze funnels, cohorts, and product metrics (D1/D7/D30 retention, playtime, conversion, ARPDAU, LTV) across the entire portfolio
Design and analyze A/B tests with genuine statistical rigor (power analysis, peeking effects, multiple comparison correction) — including declining tests that are poorly designed
Build and maintain the company's reference dashboards (Looker)
Inform kill/iterate/scale decisions for soft launches — your analyses commit real budgets
Data Scientist side
Develop and productionize predictive models: LTV prediction, churn, player segmentation, anomaly detection
Industrialize your analyses into versioned, tested, reproducible Python pipelines
Build the company's AI agents: automated soft launch analysis, intelligent alerting, and natural-language interfaces to data (MCP, LLM + SQL)
Contribute to the evolution of the data stack: modeling, quality, orchestration
5+ years of experience in data analytics or data science, including significant experience in mobile gaming or F2P apps — you know the industry's orders of magnitude without looking them up
Technical excellence: expert SQL (window functions, query optimization on large volumes) and production-grade Python (pandas, scikit-learn, testing, packaging) — not only notebooks
Solid and honest statistics: hypothesis testing, regression, causal inference — including the ability to recognize when the data does not support a conclusion, and to say so
Demonstrated product sense: you can cite concrete decisions your analyses changed, along with their impact
AI systems in production: analysis agents, text-to-SQL, RAG, or LLM pipelines — built by you, used by others, with an architecture you can explain in detail
Command of gaming analytics platforms (GameAnalytics, Adjust/AppsFlyer) and a BI tool (Looker preferred)
Fluent English required
This role requires both production engineering skills and product judgment. If your experience covers only one of the two, this position is unlikely to be the right fit.
ML models running in production at scale (batch or real-time), with monitoring
MCP servers or internal automation tools you have built
Detailed knowledge of the F2P economy (ad waterfalls, mediation, IAP, pricing)
Applied causal inference experience (uplift modeling, synthetic control)
