Data Scientist - PEP Risk

Lead the development and implementation of advanced data science models. Collaborate with stakeholders to understand requirements. Drive best practices in data science. Ensure data quality and integrity in all processes. Mentor and guide junior data scientists. Stay updated with the latest trends in data science. Design and implement advanced data science models to strengthen PEP and sanctions screening capabilities. Translate emerging regulatory requirements into actionable, data-driven solutions that can be scaled globally. Partner with Product, Engineering, Advisory, and MLRO teams to build frameworks that align with evolving compliance standards. Refine PEP list definitions to ensure comprehensive coverage and regulatory alignment. Conduct large-scale data exploration to identify optimization opportunities, improve detection quality, and reduce false positives. Lead technical mentorship, ensuring best practices in model validation, explainability, and governance. Collaborate cross-functionally to ensure real-time integration of analytical solutions into production systems. Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience. 5+ years' experience in data science, quantitative modeling, or advanced analytics within AML, sanctions, or financial crime domains. Deep understanding of global sanctions programs (OFAC, EU, UN, CSSF) and PEP screening requirements. Strong technical skills: SQL (BigQuery), Python (Pandas, NumPy, PySpark), R, Tableau, Power BI. Experience working with Lexis Nexis Sanctions and PEP data structures and sourcing. Experience with Hadoop, Hive, or Jupyter notebooks; cloud or data warehouse skills a plus. Excellent communication and collaboration skills; able to translate technical insights into clear business recommendations. Strategic thinker with curiosity, adaptability, and a drive for impact. Master's degree in a quantitative discipline (e.g., Data Science, Statistics, Computer Science). Familiarity with explainable AI, bias mitigation, and regulatory technology frameworks. Prior experience building or deploying models in production within financial services.