Senior Product Analyst / Data Scientist

The role

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.

Responsibilities

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

Requirements

• 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.

Nice to have

• 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)