Data Science Engineer – Antifraud

Hybrid - Full Time - Analytics Department

Cyprus

Application ID: 3028

Own and deliver antifraud solutions that operate in production and directly impact business outcomes. This role focuses on designing machine learning and rule-based antifraud systems, integrating them into existing decision flows, and ensuring they are reliably deployed, monitored, and continuously improved. You will work in close collaboration with Product Analysts and the Data Science team.
Position not open anymore

Key Responsibilities

  • Own the end-to-end development and productionization of antifraud solutions, collaborating with Product Analysts and the Data Science team to ensure models and rules are effectively deployed and deliver measurable impact
  • Productionize ML and rule-based antifraud models by integrating them into existing pipelines and real-time or batch decision systems
  • Monitor model performance in production and iterate based on fraud patterns, false positives, and business impact

Skills, Knowledge and Expertise

  • 4+ years of experience in Data Science, Machine Learning, or a similar role, with a strong focus on fraud detection or behavioral analysis
  • Proven experience owning antifraud solutions end-to-end, from problem definition and modeling to production deployment and iteration beyond offline experiments
  • Strong Python engineering skills, with hands-on experience writing production-oriented code for data processing, feature engineering, and model inference
  • Practical experience with Python DS and ML libraries, including pandas, NumPy, scikit-learn, and gradient boosting frameworks (e.g., LightGBM or XGBoost)
  • Experience working with highly imbalanced datasets and understanding fraud-specific trade-offs between precision, recall, and business impact
  • Hands-on experience with model evaluation and interpretability, including tools such as SHAP or equivalent approaches
  • Experience analyzing large-scale event or user-level data in batch or near–real-time environments
  • Ability to independently structure complex, ambiguous antifraud problems and deliver practical, scalable, production-ready solutions
  • Strong collaboration and communication skills, with experience working effectively alongside Product Analysts, engineering teams, and other Data Science practitioners

Conditions

  • Stable salary, official employment
  • Health insurance
  • Hybrid work mode and flexible schedule
  • Access to professional counseling services, including psychological, financial, and legal support
  • Discount club membership
  • Diverse internal training programs
  • Partially or fully paid additional training courses
  • All necessary work equipment