Senior LTV Forecasting Analyst

Stellar Tech; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Competitive salary

21 days annual leave + 10 bank-holiday

Job summary

StellarTech in the EdTech space seeks a Senior Data Analyst to own and evolve the LTV prediction model. You will prototype improvements in SQL, validate forecasts, and explain deviations to product and finance teams.

Work with Data Engineering to automate routines and explore how features, cohorts, and sources affect LTV, delivering data-driven recommendations that shape media spend and product strategy.

Qualifications

• 4+ years in data analytics focusing on product metrics.
• Strong SQL with complex queries and window functions.
• Solid statistics and understanding of ML models for forecasting.
• Experience with LTV, cohorts, ROI, CAC is a plus.

Responsibilities

• 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, 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.

Skills

SQL

Statistics

Forecasting

Data analysis

Communication

Tools

Tableau

Looker

Power BI

BigQuery

Job description

StellarTech in the EdTech space seeks a Senior Data Analyst to own and evolve the LTV prediction model. You will prototype improvements in SQL, validate forecasts, and explain deviations to product and finance teams.

Work with Data Engineering to automate routines and explore how features, cohorts, and sources affect LTV, delivering data-driven recommendations that shape media spend and product strategy.