Senior Director

Job requirements
7+ years of experience in AML model validation, transaction monitoring rule design, sanctions screening, model risk management, and financial crimes compliance, with at least 2 years of hands-on experience in blockchain-analytics settings/model review, including validating and configuring blockchain-analytics rule settings (Chainalysis, TRM Labs, Elliptic, or comparable)
Bachelor’s degree in a relevant field or international equivalent
Strong knowledge of model risk management standards / regulatory methodological fluency including:
OCC Bulletin 2026-13 (Model Risk Management: Revised Guidance) and its risk-based, principles-based approach (and SR 11-7 / OCC 2011-12)
DFS Part 504
FFIEC BSA/AML Examination Manual
OFAC “Framework for OFAC Compliance Commitments” and sanctions screening expectations
A mixture of consulting and in-house expertise, with hands-on experience across both crypto/digital-asset firms and traditional financial institutions
Demonstrated experience performing coverage assessments tailored to a client’s risk profile, designing transaction monitoring scenarios, rule implementation, and alert optimization methodologies
Demonstrated experience in sanctions screening rule tuning and list governance
Experience supporting regulatory examinations and annual compliance certifications
Excellent analytical, documentation, and stakeholder management skills
Hands-on platform experience across traditional TM, sanctions screening, and blockchain analytics systems
Strong technical and data skills, including ability to independently write and review queries to support ATL/BTL testing, rule replication, and data-lineage validation
Strong understanding of statistical validation techniques, data analysis, and model performance metrics
Familiarity with Snowflake, Databricks, or comparable warehouses; Tableau/Power BI for outcomes analysis and reporting
AI governance mindset: thinks about AI governance and is comfortable reasoning about how model-risk principles apply to AI/ML systems preferred
Prior experience with fraud risk models and graph analytics preferred
AI/ML literacy sufficient to validate ML-based TM or fraud models under model-risk principles (bias, drift, explainability) preferred
A portable book of work or referral relationships from a prior industry or consulting seat preferred

Job responsibilities
Model Risk Management & Model Validation
Own MRM and model validation engagements end-to-end — pre- and post-implementation testing, ongoing validation, and periodic re-validation — aligned to current interagency guidance, including OCC Bulletin 2026-13 and state standards such as NY-DFS Part 504
Apply a risk-based, materiality-driven approach consistent with OCC Bulletin 2026-13, tailoring the depth of validation and effective challenge to the complexity and materiality of each model and the client’s risk profile
Develop and maintain model governance documentation, validation reports, and audit-ready workpapers
Blockchain-Analytics Tool Validation (priority focus)
Hands-on validation, configuration, and tuning of tools such as Chainalysis (KYT), TRM Labs, Elliptic (Lens), and Merkle Science, including rule library configuration, exposure/typology thresholds, risk-scoring logic, and integration into TM and case-management workflows
Serve as the firm’s credible SME on validating crypto models and blockchain-analytics settings
Transaction Monitoring Program Design & Tuning
Design and calibrate rule sets and perform coverage assessments that map TM/sanctions coverage to the client’s specific business activity, products, customers, and geographies (never a one-size template)
Lead threshold calibration, above-/below-the-line (ATL/BTL) testing, and pre- and post-implementation validation, through to ongoing monitoring
Translate risk assessments into defensible detection scenarios and document the rationale for coverage decisions
Partner with Compliance, Financial Crimes, Risk, Data Science, Product, and Engineering teams to implement and enhance monitoring and screening capabilities
Sanctions Screening Validation
Validate and tune sanctions screening systems — list management, fuzzy-matching parameters, threshold tuning, filter logic testing, and name-matching algorithm review (ideally with experience across a variety of vendors)
Methodology, AI Enablement, and Cross-Training Expertise
Scalable methodology ownership: build, maintain, and continuously improve a consistent, repeatable methodology across MRM, validation, TM design, and sanctions tuning
AI-enabled delivery: leverage AI to improve the speed, consistency, and defensibility of testing, sampling, and documentation, positioning the practice for the emerging AI model-validation adjacency
Team development and cross-training: upskill existing resources to scale delivery
Client-facing polish: present findings with authority to CCO/BSAO stakeholders, boards, and regulators
Market Eminence: Monitor emerging financial crime typologies, regulatory guidance, and industry practices to continuously enhance detection capabilities, and contribute to firm thought leadership as needed