Marketing Data Analyst (GameDev)

Role overview Build predictive models that help marketing and product teams make decisions about user acquisition and the value of mobile game audiences. The central focus is forecasting lifetime value and return on ad spend, with related analysis of revenue, payback, retention, and user behavior. You’ll work independently from model design through validation and communicate what the forecasts can—and cannot—tell stakeholders.

Responsibilities - Design, build, and validate predictive models from scratch, especially for LTV and ROAS forecasting. - Forecast related measures such as revenue, traffic payback, retention, and user value. - Choose suitable statistical methods, measure prediction accuracy, and improve models as new data becomes available. - Analyze mobile user behavior, cohorts, segmentation, retention, acquisition costs, and monetization. - Assess user acquisition performance, traffic quality, and campaign profitability, then turn findings into recommendations. - Automate recurring analysis, model updates, and reporting with Python and SQL; prepare dashboards and explain model assumptions and limitations.

Requirements - At least two years of experience in data, product, or marketing analytics, data science, or a related analytical role. - Hands-on experience with user, product, or marketing data from mobile applications. - Demonstrated ability to build and validate predictive or forecasting models independently from scratch. - Strong Python skills for data analysis, statistical work, and predictive modeling. - Solid SQL skills for extracting and analyzing data.

Nice to have - Experience in game development is welcome. Relevant mobile data experience from fields such as fintech or e-commerce may also apply.

Benefits and work setup - Full-time, remote role.