Financial Crimes Compliance Modeling & Analytics Manager

Mercury•Portland, OR

5d•$149,900 - $208,300

About The Position

Mercury is building a complete finance stack for startups and is seeking a Financial Crimes Compliance Modeling & Analytics Manager to drive enhancements to its financial crimes compliance (FCC) detection and screening models and improve the overall FCC framework. This role will be key in developing, tuning, and maintaining transaction monitoring and Sanctions models, as well as building the analytics and metrics to track the health of FCC programs. The position requires a strong analytical background and FCC subject-matter expertise, with close collaboration with risk strategy, engineering, and compliance teams. Mercury is a fintech company, not an FDIC-insured bank, providing banking services through Choice Financial Group and Column N, Members FDIC.

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 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
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

Nice To Haves

Experience with Python or similar preferred
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
Curiosity about how AI/ML is being applied to financial crime detection, and openness to modern tooling as the function evolves

Responsibilities

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
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