Full-Time Game Data Analyst

At A Thinking Ape, data is a core part of how we build and grow successful games.

We've invested heavily in self-service analytics tools, enabling teams across the studio to independently access, explore, and act on data. The Data and Player Insights team powers this capability—building and maintaining data infrastructure, guiding data literacy, and leading deeper analyses that inform strategic decisions.

As a Game Data Analyst, you’ll play a key role in this ecosystem—uncovering insights, shaping product strategy, and optimizing the performance of our mobile games. Through advanced analytics and collaborative problem-solving, you’ll help enhance the player experience and drive the continued success of our live game portfolio in a fast-moving, competitive market.

Your responsibilities will fall into two core areas:

1. Empowering self-service analytics by building tools, dashboards, and models that enable teams to access and use data independently.
2. Conducting advanced analyses to answer complex questions, inform decision-making, and optimize game and player outcomes.

You’ll also support studio-wide data literacy efforts.

Key Responsibilities

Self-Service Tools & Dashboards: Develop and enhance analytics tools and dashboards to enable studio-wide access to actionable insights.

Data Analysis & Insights: Analyze player behavior and game performance using advanced techniques to generate insights that enhance player engagement and game success.

Experimentation & A/B Testing: Support the design and execution of A/B tests to generate actionable insights for game development and live operations.

Data Literacy & Best Practices: Promote data fluency across the studio by mentoring data consumers, establishing best practices, and advocating for data quality and consistency.

Cross-Functional Collaboration: Work closely with designers and live ops managers to evaluate feature and event performance, define success metrics, and guide decisions from planning through launch.

Data Pipeline Integration: Partner with data engineers to support the maintenance and evolution of data models and pipelines that power our analytics ecosystem.

Predictive Modeling: Build and validate predictive models to forecast key metrics such as LTV and churn, supporting long-term strategic planning.

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