Financial Crimes Compliance Modeling & Analytics Manager

As part of the journey, we would expect you to:
Use SQL and other analytical tools to conduct in-depth analysis of Mercury’s customers, transactions, alerts, TM rules, risk ratings, and more
Use data-driven methods to improve, design, implement, and maintain Mercury’s 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
Know how to 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

There are lots of paths that could lead you to be successful in a role like this; we think the strongest candidates will have some combination of the following:
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

Our target new hire base salary ranges for this role are the following:
US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $166,600 – $208,300
US employees outside of the New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $149,900 – $187,500
Canadian employees (any location): CAD $157,400 – $196,800