Senior Data Scientist

Own models end-to-end across the fraud suite - transaction fraud detection, 3DS friction reduction, enrolment fraud, merchant fraud, cardholder risk scoring, and NLP/LLM-based products - from design through production deployment and monitoring.Lead a team of 5–10 data scientists and engineers: set technical direction, review work, mentor junior members, and uphold engineering and modelling standards.Drive feature discovery through deep EDA - form strong hypotheses, test them rigorously, and translate findings into production features that measurably reduce fraud and false declines.Investigate client case studies: when banks report missed fraud or declines of genuine transactions, root-cause the issue in our pipeline, quantify impact, and propose model or feature fixes.Build robust data pipelines and experiments using Python, complex SQL, and distributed/orchestration tooling.Communicate with impact - present results and trade-offs to product, engineering, management, and clients; resolve conflicts within and across teams.Ensure ML best practices: version control, reproducible workflows, automated testing for data/model code, and post-deployment monitoring.We are looking forEducation: BS/MS - CS/Statistics/Mathematics/DS/EngineeringExperience: 6 - 10 years of applied data science / ML experience, including models shipped to production.SkillsExpert in Python (pandas, numpy, scikit-learn) and strong SQL for extraction, aggregation, and complex analysis.Solid grounding in supervised ML and model evaluation (imbalanced classification, calibration, feature importance/interpretability).Proven experience deploying and monitoring models in production.Demonstrated leadership / mentoring of a 5–10 person team and the ability to resolve team-level conflicts.Clear communicator able to bridge technical depth and business stakeholders.Nice-to-have (bonus)XGBoost and other gradient-boosting / deep learning frameworks.Exposure to LLMs, RAG, and Agentic AI workflows.NLP experience (e.g., scoring CSR call quality, conversational agents).Domain experience in payments, fraud, or risk - authorizations, 3DS, chargebacks, issuer processing.Experience with Data Engineering and MLOps tools (like Docker/Kubernetes, Spark, Airflow), Git, and visualization tools (matplotlib/plotly).