Senior Data Analyst (LTV / Predictive Analytics)

Location: Remote

Company

hirify.global is a B2C mobile publisher in EdTech focused on subscription apps, in-app monetization, and performance marketing.

What you will do

• Own and develop the LTV prediction algorithm by identifying factors that improve forecast accuracy and prototyping changes in SQL.
• Validate forecasts on closed cohorts, monitor model quality, and explain deviations from actual LTV.
• Research how product features, user segments, and purchase sources affect LTV and adapt prediction logic accordingly.
• Lead analytical research from hypothesis formation through documented recommendations for Product, UA, and Finance.
• Automate analytical routines and model-quality monitoring with Data Engineering.

Requirements

• 4+ years in Data Analyst, Senior Data Analyst, or Product Analyst roles with a product analytics focus.
• Strong SQL skills, including complex queries, window functions, and query optimization.
• Solid mathematical statistics and understanding of ML models, forecasting, model validation, MAE, RMSE, MAPE, and R2.
• Experience with LTV, cohorts, retention, ROI, CAC, ARPU, payback, and in-app B2C monetization, preferably subscriptions.
• Ability to investigate ambiguous questions end to end, form hypotheses, and communicate data-backed recommendations to technical and business stakeholders.
• Python, A/B testing, BI tools, and cloud data warehouses are welcome but non-critical.

Nice to have

• Feature engineering and model interpretation using SHAP or feature importance.
• Experience with mobile products, gaming, or subscription businesses.
• Familiarity with Amazon Athena, S3-based DWH, CatBoost, Tableau, Git, and Jupyter.

Culture & Benefits

• Work on LTV prediction and analytical initiatives with direct impact on company investment decisions.
• Innovative EdTech environment focused on experimentation and new solutions.
• Exposure to AI solutions.
• Flexible paid time off with 21 days of annual leave and 10 bank holidays.
• Collaborative work with Product, UA, Finance, Data Engineering, and operational leadership.

Hiring process

• Introductory call with a recruiter.
• Technical interview covering SQL, statistics, ML fundamentals, and an analytical case.
• Final interview with the department head, with an optional culture-fit conversation with HRBP.