Consultant / MRM AML

Job Description

Join Our Team: Model Risk Management Financial Crime Analytics At Deloitte , our Model Risk Management (MRM) practice helps clients navigate the complex landscape of model risk across financial crime, regulatory compliance, and advanced analytics. Our team consists of top-tier professionals with backgrounds in Statistics, Mathematics, Physics, Finance, Financial Engineering, and PhDs in quantitative disciplines . We specialize in qualitative assessments and quantitative modeling across Market Risk, Credit Risk, Operational Risk, Liquidity Risk, and Fraud Risk aligned with key regulatory standards such as CCAR/Stress Testing, Basel II.5/III, CRD IV/CRR . We also work on the valuation of complex derivatives and structured products , leveraging our deep expertise in quantitative modeling and data analytics. What You’ll Do In this role, you'll contribute to cutting-edge financial crime risk initiatives and support leading global banks in strengthening their risk modeling frameworks. Key Responsibilities: Develop and maintain Financial Crime models , including AML Feeder Models and Sanction Screening Models, using statistical methods and robust documentation practices. Tackle complex data analytics problems in risk management and deliver actionable insights to enhance risk tools. Apply deep quantitative and conceptual expertise to design, validate, and enhance risk models aligned with banking regulatory standards. Use programming tools such as Python, R, SAS, and SQL for model development and deployment. Translate complex modeling concepts into simple language for diverse stakeholders — from executives to technical experts. Create technical documentation , model validation reports, and regulatory submissions. Required Skills & Experience Sound understanding of statistical and mathematical concepts : regression, classification, time series, hypothesis testing, distribution analysis, A/B testing, performance monitoring, etc. Hands-on experience with AI/ML techniques : ANN, Decision Trees, Ensemble Methods (e.g., XGBoost). Experience with vendor-based AML systems and models (e.g., Event Processor Next Gen, Quantifind, Customer Risk Assessment). Expertise in Financial Crime Case Generation Analytics . Proficient in R, Python, SAS, SQL , with experience deploying models into production and performing data calibration. Understanding of AML and sanction screening regulatory frameworks. Strong skills in technical writing , executive reporting, and stakeholder communication. Exceptional time management and multitasking ability in a dynamic, global work environment. Flexibility to work with US/UK counterparts during extended or overlapping shifts. Preferred Skills Solid data science foundation with an understanding of end-to-end data pipelines and model lifecycles. Experience working in a banking or consulting environment , ideally on financial crime or risk analytics projects. Qualifications Bachelor’s , Master’s , or Ph.D. in a relevant quantitative field (e.g., Statistics, Mathematics, Financial Engineering, Computer Science, or related disciplines).