Summary
What you’ll impact
The Senior Model Validation Analyst validates, tests, and optimizes rule‑based and machine‑learning AML surveillance models to meet regulatory standards and improve risk detection. They use SQL and Python/R to query large transaction datasets, conduct statistical analyses, and verify data pipeline integrity. Deliverables include detailed validation reports, model tuning to reduce false positives, and documentation that supports internal audits and regulator review. Collaboration spans data engineers, software engineers, product managers, and compliance teams to ensure accurate model implementation and provide AML subject‑matter expertise.
Responsibilities
What you'll do
- Model Validation & Review Execution: Perform independent, rigorous quantitative and qualitative analyses of rule-based and machine learning financial crimes surveillance models, filtering systems, and detection logic targeting model design, algorithm soundness, data integrity, and change management
- Technical Review & Remediation: Act as a technical bridge within a fast-paced fintech environment by leveraging a solid foundation in data engineering, data modeling, and infrastructure. Collaborate seamlessly with software engineers, data engineers, and data scientists to decipher complex logic, evaluate model implementations, test data pipelines, and support the remediation of model issues.
- Optimization & Model Tuning: Execute effective tuning methodologies and statistical analyses to continuously calibrate and enhance surveillance models. Work on strategic initiatives focused on maximizing risk detection capabilities while systematically reducing false positive volumes to ensure an efficient and effective alert review process.
- Logic & Data Integrity Assurance: Conduct comprehensive data validation and logic testing initiatives to establish a strong foundation of data quality. Ensure the completeness, accuracy, and end-to-end reliability of critical data pipelines and inputs feeding compliance models, mitigating systemic risks and safeguarding the integrity of the transaction monitoring ecosystem.
- Subject Matter Expertise: Serve as a knowledgeable subject matter resource in AML and Model Risk, providing practical domain insights and supporting cross-functional partners in understanding model logic, performance, and risk trade-offs.
- Cross-Functional Collaboration: Build strong working partnerships across engineering, product, and compliance teams to ensure technical solutions are conceptually sound, effective, and aligned with risk tolerance.
- Change & Issue Management: Uphold change and issue management policies for covered systems, models, and solutions, ensuring end-to-end traceability and maintaining defensible documentation for internal audit and external regulators.
- AI Strategy & Tech-Forward Adoption: Bring hands‑on capabilities and experience leveraging AI tools, Large Language Models (LLMs), and automation techniques to enhance daily productivity, elevate deliverable quality, and optimize team workflows.
- Documentation & Reporting: Author thorough, concise, and defensible validation reports, change and issues management documentation, and executive summaries that clearly explain model theory, testing logic, performance findings, and actionable recommendations for key stakeholders.
Requirements
What you’ll bring
- Education: Bachelor's Degree or equivalent in a quantitative, technical, or financial field (e.g., Computer Science, Data Analytics, Statistics, or Finance)
- Experience: 5+ years of experience across AML, Financial Crimes, or risk management within the financial services or fintech industry, with a focus on model validation, quantitative analysis, or surveillance system management.
- Domain Expertise: 3+ years of direct experience in the verification, validation, testing, or tuning of AML rule-based systems and/or machine learning models, along with a working knowledge of core Model Risk Management (MRM) principles and governance standards.
- Technical & Analytical Skills: Proficiency in SQL and experience using quantitative tools or programming languages (e.g., Python, R) to query large datasets, perform statistical analyses, and evaluate model performance.
- Adaptability & Communication: Excellent verbal and written communication skills with the ability to explain complex technical and quantitative concepts to non-technical partners, coupled with an ability to manage multiple competing priorities in a fast-paced environment.
- Location Requirement: Role based in Denver, CO, New York, NY, or Westlake, TX with in-person attendance expected at least 3 days per week.