Financial Crimes Model Analyst

The Financial Crimes Model Analyst plays a critical role in designing, validating, and optimizing analytical models that support financial crime prevention, including fraud, AML/KYC, and sanctions monitoring. This role leverages modern data‑agnostic, low/no‑code analytical platforms and machine‑learning tooling to operationalize robust detection logic, integrate LLM/SLM outputs responsibly, and ensure alignment with Model Risk Management (MRM) standards.

Responsibilities

• Designs, validates, and enhances financial crime detection models across fraud, AML/KYC screening, and related domains.
• Applies statistical techniques to evaluate and calibrate LLM/SLM outputs when used in decision‑support workflows.
• Conducts ablation studies, back testing, and reproducible experiments to ensure model stability and business impact.
• Optimizes model and pipeline efficiency, including latency, throughput, and computational performance.
• Develops, tracks, and interprets performance metrics; implements monitoring for drift, bias, degradation, and shifts.
• Produces audit‑ready MRM documentation, including validation plans, governance artifacts, explainability notes, and calibration reports.
• Maintains model health dashboards and reporting for executives, governance bodies, and oversight functions.
• Partners with internal and external data engineers and vendor partners to maintain reliable inputs and production workflows.
• Develops SQL and Python assets for exploration, experimentation, reporting, and automated artifact generation.
• Maintains clear and comprehensive documentation, including data dictionaries, model cards, decision logic, and workflow diagrams.
• Works cross‑functionally with engineering, product, compliance, risk, and vendor teams to deploy and maintain models.
• Translates complex analytical concepts into accessible insights for varied technical and business audiences.
• Performs other duties as assigned.

Qualifications

• Bachelor’s degree in Statistics, Econometrics, Data Science, Mathematics, Computer Science, or a related quantitative field (required); Master’s degree preferred.
• 3–5 years of experience in model development, evaluation, monitoring, risk governance, or model lifecycle management supporting BSA/AML compliance, fraud and/or case investigation, or experience in quality assurance/control or internal audit (required).
• Hands‑on expertise in SQL and Python for analysis and rapid prototyping (required).
• Familiarity with modern modeling techniques or low/no‑code platforms and responsible use of LLM/SLM capabilities (required).
• Experience producing governance‑grade validation or audit documentation (required).
• Exposure to financial crime domains (AML, KYC, fraud) preferred.
• CAM​S and/or CAFP certifications preferred.

Knowledge, Skills, and Abilities

• Strong understanding of supervised/unsupervised machine‑learning evaluation, calibration, drift detection, and explainability methods.
• Knowledge of data quality domains and data lineage principles.
• Familiarity with AI model governance concepts, including Model Risk Management standards and regulatory expectations.
• Ability to develop and maintain dashboards and reports for model health and risk indicators.
• Knowledge of regulatory environments and emerging BSA/AML and fraud trends.
• Strong investigative, written, and oral communication skills.
• Thorough, detail‑oriented, and committed to ethics.
• Understanding of the banking industry, including partnerships with fintech companies.
• Effective multitasking, independent and team‑oriented action, and ability to handle highly confidential information with discretion in a high‑volume, fast‑paced environment.