Data Analyst - Economy & Segmentation

Join a top-tier mobile gaming team with millions of downloads worldwide and exciting new titles on the way. We're looking for a Data Analyst who’s passionate about digging into player behavior and game economy data, with strong hands-on skills in Predictions and segmentation.

You’ll play a key role in shaping engaging and well-balanced experiences by analyzing how different player types interact with core loops, progression systems, and monetization mechanics.

This is a cross-functional role where you'll collaborate closely with game economy designers, product managers, and developers to bring clarity, structure, and insights into how our economies serve different segments of players.

What You’ll Do

Build and maintain player segmentation models using gameplay, behavioral, and monetization signals.
Analyze player behavior, level progression, and feature usage to surface actionable insights.
Partner with game economy and product teams to fine-tune sources, sinks, pacing, and offers based on segment needs.
Use Python and SQL to run deep-dive analyses, build pipelines, and automate recurring economy metrics.
Design and evaluate A/B tests related to game economy, pacing, and progression.
Present clear findings via dashboards and storytelling that inform product and economy decisions.

Requirements:

2+ years of experience as a data analyst, data scientist or economist (preferably in gaming).
Proven experience with Python for data analysis, segmentation, and predictions.
Strong command of SQL and experience exploring large datasets.
Understanding of game economy concepts, player motivations, and progression systems.
Experience with BI tools (e.g., Power BI, Looker, Tableau).
Clear communication and collaboration skills – able to work with technical and non-technical teams.

Bonus Points For

Prior work on segmentation frameworks for different player types.
Experience with level analytics, offer targeting, or monetization modeling.
Statistical testing and experimentation at scale.