Data Scientist

📋 Description Define and build predictive models for churn, upsell, and CLV. Work with trading, transactional, and behavioral data; engineer features. Own the full modeling lifecycle: target definition, training, validation, monitoring. Deliver daily client-level scores integrated into CRM workflows. Translate model outputs into actionable insights for non-technical sales teams. Collaborate with sales leadership to design interventions and present results. 🎯 Requirements 4+ years building and deploying predictive models (classification, regression, scoring) Strong Python (pandas, scikit-learn, XGBoost/LightGBM/CatBoost) and SQL. Frame ambiguous problems as ML tasks; define targets; engineer features. Experience with tabular data at scale: feature engineering, class imbalance, validation. Ability to communicate model results to non-technical stakeholders. Experience with time-series or event-based behavioral data. 🎁 Benefits Flexible benefits package. Work-from-home, office, or hybrid options. Collaborative, informal culture. Ongoing training and development. Global exposure with teams worldwide. Company-sponsored events and networking.