Senior Data Scientist - Personalization & Predictions

Role overview Join an AI team that owns the algorithmic core of a personalization platform, working across predictions, contextual personalization, contextual bandits, autosegmentation, uplift modelling, and the agentic workflows that power the system. The mission is to extract signal from how customers of more than 1,400 brands actually behave, and to prove with evidence that a model moved a real business metric. This opening is centred on the modelling and data-science side of the team, with room to dip into ML product engineering or platform/MLOps depending on interest.

Responsibilities - Translate ambiguous business questions into measurable ML problems before opening a notebook. - Mine behavioural, catalogue, and event-stream data at terabyte scale inside BigQuery and Databricks for features with genuine predictive signal. - Build and evaluate models for propensity, churn, contextual bandits, autosegmentation, and uplift or incrementality. - Design the evaluation story end to end: offline metrics, backtests, and the A/B framework that decides whether a model ships. - Hand models off to ML Engineering with clear documentation and a working conversation, not a thrown-over-the-wall notebook. - Explain findings and trade-offs to Product, Engineering leadership, and occasionally external stakeholders.

Requirements - 5+ years building ML models that have actually shipped to production. - Strong feature engineering and experimentation design skills, plus the statistical literacy to judge whether a number means anything. - Experience working with behavioural data, event streams, or other large-scale data infrastructure. - Ability to read research literature, run a quick PoC, and separate real results from well-marketed ones. - Clear written and verbal communication for cross-functional audiences. - Self-directed ownership of the question "how would we know if this is worse?".

Nice to have - Background in contextual bandits, uplift modelling, or incrementality testing. - Comfort owning an ML-powered feature end to end, with the ML piece being the interesting part.

Benefits and work setup - Remote-first flexibility across Central & Eastern Europe, with optional in-office presence in Bratislava, Brno, or Prague. - 5 paid volunteering days per year. - $1,500 annual professional education budget for books, courses, or certifications. - Subscription to a sleep and meditation app, plus an Employee Assistance Program with counsellors. - Extended parental leave of up to 26 calendar weeks for primary caregivers. - Quarterly global DisConnect days and regular company events across regions. - Restricted Stock Units or Stock Options depending on role and seniority, plus a company performance bonus. - Employee referral bonus of up to $3,000.