Data Scientist - Sanctions 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. Lead data science initiatives that improve sanctions screening algorithms and optimize detection sensitivity. Design and implement data-driven solutions that balance detection precision and false positive reduction. Collaborate with advisory, product, and engineering teams to design frameworks supporting sanctions compliance. Translate regulatory requirements into actionable analytical frameworks and technical solutions. Modernize screening platforms using big data and ML-driven approaches to increase scalability and speed. Conduct advanced analytics and identify workflow inefficiencies, recommending actionable improvements. Collaborate with engineering teams to ensure deployment, monitoring, and continuous improvement in production systems. Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience. Bachelor's degree with a minimum of six years of experience in sanctions transaction screening, profile screening, or AML, including at least three years in data analytics or data science. Strong understanding of economic sanctions programs administered by OFAC, EU, UN, CSSF, etc., and associated adjacency risk. Proficiency in SQL, BigQuery, Python, R, Tableau, and Power BI, with strong analytical and problem-solving skills. Working knowledge of Hadoop, Hive, Jupyter Notebooks, and data warehouse technologies. Excellent written and verbal communication skills, with the ability to translate analytical insights into actionable business recommendations. Proven ability to manage multiple projects in a fast-paced, dynamic environment with independence and initiative. Advanced degree in Data Science, Statistics, or a related field. Experience deploying and maintaining ML models in production within compliance or regulated environments. Familiarity with explainable AI, model validation, and governance frameworks.