Data & Analytics

Pocket Worlds•Austin, TX

18h

About The Position

This role owns the full analytics loop for a live-service game: framing the right questions, producing trustworthy numbers, building repeatable tools, and turning findings into decisions. It combines analysis, analytics engineering, and internal-tools development. AI coding agents provide much of the hands-on leverage, while the role supplies judgment about what to measure, whether results are plausible, and when further investigation is needed. The role covers analysis of content releases, live events, monetization, the in-game economy, and player behavior. It also builds and maintains internal dashboards, tools, data pipelines, models, and simulations, and makes these usable beyond the data team through documentation, APIs, and integrations. Findings are translated into recommendations for product, art, live operations, and external partners, with follow-through on resulting changes.

Requirements

Strong SQL and Python skills for analysis.
Applied statistics skills and practical judgment about methods for noisy, skewed, real-world data and their limitations.
Comfort building and deploying small services and data pipelines.
Fluency with AI coding agents and related workflows.
Ability to investigate results that seem implausible before reporting them.
Ability to set up analyses that answer the intended question, including comparing like with like, separating real effects from noise or changes in the measured population, and recognizing when data cannot support a conclusion.
Understanding of free-to-play games, including monetization (such as gacha), live events, virtual economies, and player-segment behavior.
Ability to explain methods in plain language and write for decision-makers, emphasizing interpretation rather than restating numbers.

Nice To Haves

Familiarity with applied machine learning, including embeddings, similarity, and scoring.

Responsibilities

Own analytics across content releases, live events, monetization, the in-game economy, and player behavior, from ambiguous questions through clear recommendations.
Write complex SQL against large, messy, semi-structured event data.
Design and evaluate A/B tests, and draw sound conclusions when a clean experiment is not possible.
Reconcile metrics across sources and explain discrepancies.
Use AI agents as a primary source of leverage for analysis, engineering, and reporting, while critically evaluating their output.
Build shared context and processes that make AI-assisted analysis reliable for yourself and others.
Help less technical teammates answer their own questions safely.
Build bespoke internal dashboards and tools around real decisions, and iterate with users after launch.
Own internal tools end to end, including data pipelines, interfaces, deployment, and iteration.
Build and maintain scheduled pipelines that feed internal tools.
Build models and simulations as needed, including forecasting, economy and event simulation, probability and expected-value tools, and ML-based content scoring.
Make tools usable beyond the data team with clear documentation, APIs, and integrations.
Turn findings into concrete recommendations for product, art, live operations, and external partners, and follow through to shipped changes.
Own recurring performance reporting and target tracking, and explain what drives results above or below plan.
Act as a thought partner on design and planning decisions.