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Pocket Worlds is seeking a Data & Analytics professional to own the complete analytics lifecycle for a live-service game. This role bridges analysis, analytics engineering, and internal-tools development, with AI coding agents handling much of the hands-on execution while you provide critical judgment on what to measure, data quality, and decision-making.
You will own analytics across content releases, live events, monetization, in-game economy, and player behavior—from framing ambiguous questions to delivering clear recommendations. Key responsibilities include writing complex SQL against large, semi-structured event data; designing and evaluating A/B tests; reconciling metrics across sources; and explaining discrepancies between datasets.
You'll leverage AI agents as your primary source of leverage for analysis, engineering, and reporting, while maintaining a critical eye on their output to catch flawed logic, data issues, or analyses that answer the wrong question. You'll build shared context and processes that make AI-assisted analysis reliable for yourself and less technical teammates.
On the tools and infrastructure side, you'll design bespoke internal dashboards and tools tailored to actual decision-making workflows rather than generic reporting. Success is measured by adoption—you'll work closely with users to understand their needs and iterate after launch. You'll own these tools end-to-end: data pipelines, interface, deployment, and ongoing iteration. You'll also build and maintain scheduled pipelines, create models and simulations (forecasting, economy simulation, probability tools, ML-based content scoring), and ensure tools are usable beyond the data team through clear documentation, APIs, and integrations.
Finally, you'll translate findings into concrete recommendations for product, art, live ops, and external partners, seeing them through to shipped changes. You'll own recurring performance reporting and target tracking, explaining what drives results above or below plan, and act as a thought partner on design and planning decisions.
Requirements:
- Strong SQL and Python for analysis
- Applied statistics with practical understanding of methods suited to noisy, skewed, real-world data and their limitations
- Comfort building and deploying small services and data pipelines
- Fluency with AI coding agents and associated workflows
- Familiarity with applied ML (embeddings, similarity, scoring) is a plus
- Ability to notice when numbers don't make sense and investigate before reporting
- Skill in setting up analyses that answer the actual question: comparing like with like, separating real effects from noise, recognizing data limitations
- Understanding of free-to-play games: monetization (including gacha), live events, virtual economies, and player segment behavior
- Ability to explain methods in plain language and write for decision-makers with interpretation, not just restated numbers
