Build and maintain ML models across the core portfolio: CLTV, churn prediction, propensity to buy, and Next Most Likely Product (NMLP)
Develop fraud detection models including transaction-level classifiers, merchant behaviour anomaly detectors, and new-account risk scorers
Contribute scored model outputs to the Next Best Action (NBA) decisioning layer that selects the optimal action for each merchant across Sales, Marketing, and in-product touchpoints
Support A/B experiments, uplift tests, and multi-armed bandit evaluations to measure the incremental impact of model-driven interventions
Design and implement end-to-end ML pipelines — from data ingestion and feature engineering through to model training, evaluation, and deployment
Monitor deployed models in production: detect performance degradation, data drift, and data quality issues; iterate and document changes proactively
Collaborate with business teams across Sales, Marketing, Risk, Operations, and Product to translate business problems into well-defined data science solutions
Run rigorous experiments and communicate findings clearly to both technical and non-technical stakeholders
Contribute to LLM-powered agentic workflows using tool-use patterns (RAG, function calling, memory) and frameworks such as LangChain or LlamaIndex
Contribute to team documentation: model cards, methodology write-ups, and internal playbooks that help the team scale its practices

3–5 years of hands-on applied data science, machine learning or statistical modelling experience in a commercial setting, with models shipped and measured in production.
Strong proficiency in Python for data science: pandas, numpy, scikit-learn, XGBoost / LightGBM, and at least one deep learning framework (PyTorch or TensorFlow).
Solid grounding in supervised and unsupervised learning: classification, regression, clustering, survival analysis, and time-series modelling.
Demonstrable experience building at least one of: CLTV, churn, fraud detection, propensity, or uplift models in a production environment.
Comfort working with large-scale structured and semi-structured data; proficient in SQL and cloud data warehouses
GCP and BigQuery strongly preferred.
Familiarity with ML experiment tracking platforms (MLflow, Weights & Biases) and model serving patterns (REST APIs, batch inference pipelines).
Working knowledge of LLM APIs (OpenAI, Anthropic, etc.) and at least one agentic AI framework (LangChain, LlamaIndex, AutoGen, or similar).
Understanding of responsible AI: fairness assessment, model explainability methods (SHAP, LIME), bias detection and mitigation strategies.
Clear communication — able to distil statistical findings into actionable insights for both technical peers and business stakeholders.

Excellent compensation package
25 days annual paid leave (+1 day per year up to 30)
Full “Luxury” package health insurance including dental care and optical glasses
Meal vouchers of 102.26 EUR per month
Fully covered Multisport card
Fully covered public transport pass for Sofia
Free coffee, snacks and drinks at the office

Data Scientist

Key skills