Senior Data Analyst

StellarTech

Що робити

Own and develop the LTV prediction algorithm: find the parameters and factors that increase forecast accuracy, prototype improvements in SQL, measure the gain, then hand the logic over to Data Engineering.
Validate forecasts on closed cohorts, monitor forecast quality over time and explain where exactly a model deviates from actual LTV.
Research what moves LTV: product features, segments, purchase sources (for example, purchases coming through CRM vs other sources), and adapt the prediction logic to them.
Run analytical research end to end, from a vague question to a documented recommendation for Product, UA and Finance: where payback improves, where to concentrate media buying, what changes unit economics at company level.
Automate analytical routines and model quality monitoring together with Data Engineering.

Critical
4+ years in Data Analyst / Senior Data Analyst / Product Analyst roles with a product analytics focus: measuring the impact of factors on business metrics, not marketing attribution.
Strong SQL: complex queries, window functions, optimisation. Here SQL is the main instrument for prototyping and testing the algorithm.
Solid mathematical statistics and understanding of how ML models work: you can build and validate a forecast, judge model quality (MAE, RMSE, MAPE, R2), run factor analysis and explain where a model broke.
Product background with in-app monetization and its economics: LTV, cohorts, retention, ROI, CAC, ARPU, payback. Subscriptions are ideal; other in-app B2C models also work. Ad-only monetization is a different pattern and is not a fit.