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.

PRINCIPAL DUTIES AND 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.

Non-Essential Functions: Performs other duties as assigned.

EDUCATION AND/OR EXPERIENCE

• Bachelor’s degree in Statistics, Econometrics, Data Science, Mathematics, Computer Science, or a related quantitative field, required; Master’s degree, preferred.
• 3-5 years’ 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 similar low/no-code platforms, and the 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.
• CAMS and/or CAFP certifications, preferred.

KNOWLEDGE, SKILLS, AND ABILITIES

• Strong understanding of supervised/unsupervised ML evaluation, calibration, drift detection, and explainability methods.
• Knowledge of data quality domains and data lineage principles.
• Familiarity with AI model governance concepts, including MRM standards and regulatory expectations.
• Ability to develop and maintain dashboards and report for model health and risk indicators.
• Knowledge of regulatory environment(s) and emerging BSA/AML and fraud trends.
• Strong investigative, written, and oral communication skills.
• Thorough and detail-oriented.
• Strong commitment to ethics, and the ability to understand a variety of issues and perspectives.
• Understanding of the banking industry, including bank partnerships with fintech companies.
• Multitasks effectively and takes action promptly, both independently and in a team environment.
• Handles highly confidential information with appropriate discretion, and works well in a high volume, fast paced environment.