Financial Crimes Compliance Modeling & Analytics Manager

Responsibilities

• Use SQL and other analytical tools to conduct in-depth analysis of customers, transactions, alerts, TM rules, risk ratings, and more.
• Use data-driven methods to improve, design, implement, and maintain FCC models, including transaction monitoring, sanctions screening, and relevant models.
• Develop bespoke transaction monitoring rules and sanctions screening logic designed to address Mercury's specific AML and sanctions risk.
• Partner with Compliance, Product, and Data leaders to translate regulatory requirements into effective analytical frameworks.
• Tell stories with data, enabling people to understand the output and meaning of analytics activities in a clear, compelling manner.
• Interpret analytics outputs to pinpoint which alerts, patterns, or anomalies signal genuine risk, and articulate why they matter to compliance and business stakeholders.
• Develop and maintain detailed documentation on the configuration of FCC models including scenarios, thresholds, segments, tuning, false positive rules, etc., and any changes made to those configurations over time.
• Evaluate and tune existing detection models and rules to reduce false positives while maintaining regulatory rigor.
• Develop data-driven methods to identify new typologies, emerging risks, and evolving financial crime trends.
• Partner with Model Risk Management to support validation and performance monitoring of models to ensure compliance with internal and regulatory standards.

Requirements

• Bachelor's degree in a quantitative field (e.g. Computer Science, Engineering, Statistics, Mathematics, or related) with 8+ years of experience conducting in-depth data analytics, ideally with 5+ years in FCC or AML/Sanctions related analytics roles.
• Deep understanding of AML and Sanctions fundamentals, including both principles and regulations.
• Outstanding skills with standard analytical tools; top-notch SQL skills required, experience with Python or similar preferred, and familiarity with modern ML tooling (e.g. scikit-learn, XGBoost) a plus.
• Experience developing, tuning, and maintaining machine learning or rule-based detection models, with an understanding of how to rigorously challenge model performance and limitations.
• Experience identifying ways to improve both data-related and operational efficiencies.
• A healthy dose of skepticism combined with a constructive, solution-oriented approach.
• Comfort operating with ambiguity and capable of synthesizing fragmented technical, operational, and business context into a clear understanding of how models actually work, even without a complete playbook.
• High agency and adaptability, able to find the highest-leverage work in a fast-moving environment with evolving priorities.
• Curiosity about how AI/ML is being applied to financial crime detection, and openness to modern tooling as the function evolves.
• Exceptional attention to detail across documentation, testing artifacts, and quantitative analysis.
• Strong written and verbal communication skills; you can explain model risk and analytics findings to both technical and non-technical stakeholders.