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Tier-1 brand, mid-level data scientist title, and metro/hybrid location increase applicant competition.
Role requires AML/sanctions domain expertise with specific ML/NLP skills, limiting cross-industry transferability.
Explicit 5+ years plus domain-specific ML, NLP, and sanctions expertise creates rigid shortlisting filters.
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Develop and maintain statistical and machine learning models for AML and Sanctions Compliance, focusing on sanctions screening and name-matching.
Perform large scale data analysis, including feature design, model performance monitoring, and tuning to improve detection accuracy and reduce false positives.
Collaborate with Compliance, Model Risk Management, and Technology teams to ensure model validation, regulatory compliance, and thorough documentation for audits and reviews.
Bachelor's or Master’s Degree in Engineering, Economics, Statistics, Mathematics, or related technical discipline.
Minimum 5 years of experience in data analysis, statistical analysis and machine learning model development using tools like SAS, SQL, Python/R, Hive or Spark.
Experience with sanctions screening and name-matching including fuzzy matching, NLP techniques, and familiarity with sanctions platforms (e.g., Fircosoft, ComplyAdvantage, World-Check, Actimize).
Strong knowledge of global sanctions regimes (OFAC, EU, UN, UK), AML, and CFT frameworks; hybrid work location model with office days to be confirmed.
Experienced in hands-on end-to-end data science lifecycle in financial crime compliance, with a focus on sanctions screening and AML.
Comfortable working in a fast-paced, high-pressure global team environment with cross-functional collaboration across business and technical stakeholders.
Skilled in advanced AI/ML techniques including generative AI applications and expert in managing regulatory and model risk management standards.