Data Scientist

Fit4Me is looking for a Data Scientist who will help the Analytics team turn data into precise forecasts that drive business decisions.
You will own the development of LTV and churn models, fraud and anomaly detection in payment data, and the translation of statistical results into recommendations for the Head of Analytics and the product team.

You will work with data end-to-end — from hypothesis to model in production. Final prioritization decisions sit with the Head of Analytics, but your models form the foundation for ROAS optimization and business forecasting.

Impact Areas:

• Build and maintain ML models (T+72h, T+7d) — from methodology design to production.
• Drive feature engineering based on GBQ, Amplitude, payment, and marketing data.
• Develop fraud / anomaly detection models for payment flows.
• Design and run statistical analysis of A/B tests — from hypothesis to interpretation.
• Build and monitor ML pipelines in a production environment.
• Document methodology for non-technical stakeholders.
• Partner closely with the Head of Analytics, analysts, and the product team.

What we expect from you:

• 5+ years of commercial experience in Data Science / ML analytics.
• Strong command of Python (pandas, scikit-learn, statsmodels).
• Advanced SQL and hands-on experience with BigQuery.
• Experience building predictive models (LTV, churn, fraud) shipped to production.
• Solid statistical foundation: A/B testing, power analysis, statistics.
• Experience working with event-based data (Amplitude or similar).
• Confident with Git.
• Understanding of ML pipelines and MLOps practices.
• Upper-Intermediate (B2) English or above, for working with documentation.

Nice to have:

• Experience in subscription / mobile app businesses.
• Experience with fitness products.
• Fraud detection in payment systems (Stripe, Adyen).
• Familiarity with Tableau or other BI tools.
• Experience with DBT.

This role is a great fit if you:

• Have hands-on experience building a predictive LTV or revenue-forecasting model shipped to production with minimal error — this is a decisive factor for us.
• Want to independently form hypotheses, choose the methodology, and own a model from design through production.
• Can explain complex models to non-technical stakeholders in a way that drives business decisions.
• Focus on business outcomes, not just technical elegance.
• Are ready for regular syncs with the Head of Analytics and working at a fast growth pace.

Hiring process:
Recruiter Interview → Technical Interview with Head of Analytics → Final Interview with HRD → Job Offer 🚀