Lead Data Scientist

Lead Data Scientist

Betty Technology

Sofia, Bulgaria

Key Responsibilities

• Design, build, test, and deploy predictive and prescriptive models (e.g., churn prediction, player lifetime value, segmentation, risk classification, fraud detection);
• Analyze large-scale player behavioral data to identify key value drivers and recommend optimizations for promotions, engagement, and monetization strategies;
• Develop models for anomaly detection, fraud prevention, and responsible gaming interventions to maintain a safe and compliant environment;
• Design and evaluate A/B and multivariate tests for product features and marketing campaigns, ensuring statistically robust insights;
• Partner with Data Engineers to ensure model pipelines are production-ready, automated, and scalable across cloud environments;
• Visualize and present analytical findings to both technical and non-technical stakeholders, driving clear, data-informed decisions;
• Monitor and refine model performance, automate recurring analyses, and ensure data quality and reproducibility.

Requirements

• 3+ years of experience in a Data Scientist, Machine Learning Engineer, or advanced analytics role (ideally within iGaming, FinTech, or other consumer-facing digital products);
• Strong theoretical understanding of machine learning and statistical algorithms (classification, regression, clustering, optimization);
• Proven experience applying models to solve business problems (e.g., forecasting, segmentation, retention, player value optimization);
• Skilled in Python (Pandas, NumPy, Scikit-learn, PySpark) and SQL (advanced querying, ETL processes, performance optimization);
• Experienced in analyzing massive datasets (100M+ rows) across multiple sources;
• Strong communication skills — able to explain complex technical concepts to non-technical audiences.

Nice to Have

• Experience with AWS, Docker, Kubernetes, or other cloud and containerization technologies;
• Background in NLP, time-series, or causal inference modeling;
• Proficiency with data visualization tools (Tableau, Looker, or Power BI);
• Experience integrating data and models into production pipelines and dashboards;
• Familiarity with iGaming KPIs such as LTV, ARPDAU, Churn, and Player Retention metrics;
• Understanding of A/B testing frameworks and experimental design.