Job Description Summary

The Manager, Financial Crimes Analytics is responsible for designing, testing, tuning, and optimizing financial crimes detection models across Anti-Money Laundering (AML), Fraud, Know Your Customer (KYC), and Sanctions programs. As a senior individual contributor, the role provides technical leadership while remaining hands-on in surveillance model development, statistical analysis, threshold calibration, model efficacy testing, and AI/ML optimization. The position partners with Financial Crimes stakeholders, Technology, Model Risk Management, and other business partners to strengthen detection effectiveness, establish consistent testing practices, and support scalable model capabilities.

Job Description

Essential Duties and Responsibilities

·         Serve as the technical lead for complex surveillance model design, development, testing, and optimization across Financial Crimes domains.

·         Design, code, test, and refine detection scenarios, statistical models, thresholds, segmentation, and risk-scoring methodologies.

·         Lead above-the-line (ATL), below-the-line (BTL), back-testing, and model efficacy assessments to identify coverage gaps and tuning opportunities.

·         Develop and monitor model performance metrics, health indicators, and early-warning processes; investigate performance deterioration and recommend corrective action.

·         Configure, test, tune, and optimize financial crimes detection platforms, including Fiserv Financial Crime Risk Management (FCRM) and LexisNexis Bridger; support integrations with Fiserv Payments Exchange, PEP+, Signature, and KYC platforms such as Fenergo.

·         Apply statistical methods to model development, feature selection, tuning, validation support, and performance evaluation.

·         Establish consistent standards for model testing, tuning, documentation, change control, and reproducible analytical results.

·         Perform hands-on root cause analysis of missed or non-detected activity, false positives, and model/data defects.

·         Partner with Model Risk Management and stakeholders on model governance, independent validation, audit, regulatory, and legal requests.

·         Present model performance findings, technical recommendations, and risk-based tradeoffs to Financial Crimes leadership.

Knowledge of

·         Financial Crimes detection and regulatory expectations across AML, Fraud, KYC, and Sanctions.

·         Surveillance model lifecycle, scenario logic, threshold calibration, segmentation, and model risk governance.

·         Statistics, predictive analytics, machine learning, and model performance measurement.

·         Financial crimes and screening platforms such as Fiserv FCRM, LexisNexis Bridger, and Fenergo, as well as Fiserv Payments Exchange, PEP+, and Signature and their related data flows and integrations.

·         Data quality, feature engineering, and analytical testing methodologies.

Skill in

·         Advanced SQL, Python, statistical analysis, and model development/testing frameworks.

·         Developing and tuning rules-based, statistical, and AI/ML detection models.

·         ATL/BTL testing, back-testing, sensitivity analysis, and detection efficacy assessment.

·         Analyzing large datasets, engineering/modeling features, and diagnosing model performance issues.

·         Hands-on configuration, scenario/rule tuning, testing, troubleshooting, and performance optimization of Fiserv FCRM, LexisNexis Bridger, and comparable financial crimes tools.

·         Communicating complex analytical findings to technical, business, and governance stakeholders.

Ability to

·         Balance hands-on model development with technical leadership and cross-domain priorities.

·         Independently lead complex model initiatives without direct people-management authority.

·         Translate emerging financial crime risks into measurable detection strategies and model enhancements.

·         Exercise sound judgment on detection effectiveness, operational impact, and model risk.

·         Drive consistent testing standards, continuous tuning, and knowledge sharing across domains.

Education / Previous Experience

·         Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Finance, Business, or a related field.

·         Six (6) or more years of experience in financial crimes analytics, model development, data science, quantitative analytics, or related work in financial services, FinTech, or consulting.

Licenses / Certifications

·         Relevant data science, machine learning, cloud, or financial crimes certifications are a plus but not required.

Education

Work Experience

Certifications

Travel

Less than 25%

Workstyle

Resident

The total compensation for this position includes base salary or wages, and may include components such as additional compensation (cash or equity), discretionary bonuses, or commissions. This position is eligible for a benefits package that may include medical, dental, and vision; life insurance; critical illness insurance and accident insurance; disability benefits; retirement savings; paid time off (including vacation, holidays, and sick leave); and parental leave.  Eligibility for benefits and specific offerings may vary based on position and employment status. To view more details of the benefits offered, visit Myrjbenefits.com.

 

 

At Raymond James our associates use five guiding behaviors (Develop, Collaborate, Decide, Deliver, Improve) to deliver on the firm's core values of client-first, integrity, independence and a conservative, long-term view. 

We expect our associates at all levels to:
•  Grow professionally and inspire others to do the same
•  Work with and through others to achieve desired outcomes
•  Make prompt, pragmatic choices and act with the client in mind
•  Take ownership and hold themselves and others accountable for delivering results that matter
•  Contribute to the continuous evolution of the firm

At Raymond James – as part of our people-first culture, we honor, value, and respect the uniqueness, experiences, and backgrounds of all of our Associates.  When associates bring their best authentic selves, our organization, clients, and communities thrive. The Company is an equal opportunity employer and makes all employment decisions on the basis of merit and business needs. 

#LI-AF1