Data Analyst
stellartech Open · verified Oct 24, 2026 At a glance
Mid-level Data Analysis & BI role at stellartech. Contract.
Pay not stated Data Analyst salary
dataroles summary, based on the employer's posting.
What you'll do
- Refine the LTV forecasting method by testing factors in SQL, measuring accuracy changes and transferring proven logic to Data Engineering
- Compare predictions with completed cohorts, follow forecast quality and identify where estimates differ from realized LTV
- Study how product features, user groups and purchase sources shape LTV, then adapt the forecasting approach
- Turn broad questions into documented advice for Product, UA and Finance on payback, media investment and unit economics
- Work with Data Engineering to automate analysis routines and checks on model quality
What you bring
- At least four years in product-focused data or product analysis, assessing business metric drivers rather than marketing attribution
- Strong SQL skills for complex queries, window functions and query optimization
- You can build and check forecasts, assess model quality with statistical measures and explain why predictions fail
- Experience with in-app monetization economics, including LTV, retention, ROI and payback, in subscriptions or another B2C model
- You can investigate an open-ended question, support conclusions with data and explain them to technical and business audiences
Who this fits
This role suits an experienced analyst who wants to focus on research and predictive analytics rather than reporting, and is comfortable with little routine direction. You’ll work with Product, UA, Finance and Data Engineering, reporting to the Operational Department head. Python is useful but optional, and the position is contractor-based.
From the employer
Data Analyst (LTV / Predictive Analytics)
StellarTech is a B2C mobile publisher in EdTech: subscription apps, in-app monetization and performance marketing at scale. LTV prediction is how we decide where to invest, and the forecasting pipeline is already live. We are opening this role to have one senior analyst own its accuracy and its evolution. This is research and predictive analytics, not reporting: everything that can be standardised is already automated by Data Engineering, so the work is hypotheses, factor analysis and improvements to the prediction algorithm itself. You report to the Head of the Operational Department and work directly with Product, UA, Finance and Data Engineering.
What you will do
- 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.
Requirements
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.
- A proven end-to-end pattern: you take a vague question, form a hypothesis, dig into it and bring it to a recommendation. There is no micromanagement here and almost no standard tasks.
- Ability to back conclusions with data and to communicate them to both technical and business stakeholders.
Non-critical
- Python (pandas, numpy, scikit-learn) for analysis and modelling: welcome, but not a blocker if the statistical and analytical thinking is there.
- A/B testing and statistical hypothesis testing.
- BI tools (Tableau, Looker, Power BI).
- Cloud DWH and large data volumes (Athena, BigQuery, Snowflake, Redshift or similar).
Will be a plus
- Feature engineering and model interpretation (SHAP, feature importance).
- Mobile products, gaming or subscription businesses.
- Our stack: Amazon Athena, S3-based DWH, CatBoost, Tableau, Git, Jupyter.
Why You'll Love Working With Us
- Impactful Work: Your contributions will directly shape the future of our company.
- Innovative Environment: We're all about trying new things and pushing the envelope in EdTech.
- AI solutions
- Wealth: We offer a competitive salary.
- Balance: Flexible paid time off, you get 21 days of annual leave + 10 bank holidays.
- Collaborative Culture: Work alongside passionate professionals who are as driven as you are.
Hiring process
- Intro call with the recruiter.
- Technical interview: SQL, statistics and ML fundamentals, plus an analytical case on how you build a plan to investigate a problem.
- Final interview with the department head. Optionally, a 30-minute culture fit conversation with HRBP.
If this is the kind of work you want to own, send us your CV. We will come back to you with the next step.
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