Manager, Financial Crimes Analytics

Raymond James•Pittsburgh, FL

11d•Onsite

About The Position

The Manager, Financial Crimes Analytics is a senior individual contributor who provides technical leadership while remaining hands-on in the design, development, testing, tuning, and optimization of financial crimes detection models across Anti-Money Laundering (AML), Fraud, Know Your Customer (KYC), and Sanctions programs. The role uses statistical analysis, threshold calibration, model efficacy testing, and AI/ML optimization to strengthen detection effectiveness and build scalable model capabilities. The position partners with Financial Crimes stakeholders, Technology, Model Risk Management, and other business partners. It establishes consistent practices for model testing, tuning, documentation, change control, and reproducible analytical results; supports governance, validation, audit, regulatory, and legal requests; and presents performance findings and risk-based recommendations to Financial Crimes leadership. The role leads complex initiatives without direct people-management authority. The position requires a bachelor’s degree in a relevant discipline and at least six years of related experience. Travel is less than 25%, and the listed workstyle is Resident. Compensation may include base salary or wages and may include additional compensation, discretionary bonuses, or commissions. Benefits may include medical, dental, and vision coverage; life, critical illness, accident, and disability insurance; retirement savings; paid time off; and parental leave. Eligibility and specific offerings may vary by position and employment status.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Finance, Business, or a related field.
Six or more years of experience in financial crimes analytics, model development, data science, quantitative analytics, or related work in financial services, FinTech, or consulting.
Knowledge of financial crimes detection and regulatory expectations across AML, Fraud, KYC, and Sanctions.
Knowledge of the surveillance model lifecycle, scenario logic, threshold calibration, segmentation, and model risk governance.
Knowledge of statistics, predictive analytics, machine learning, and model performance measurement.
Knowledge of financial crimes and screening platforms, including Fiserv FCRM, LexisNexis Bridger, and Fenergo, as well as Fiserv Payments Exchange, PEP+, Signature, and related data flows and integrations.
Knowledge of data quality, feature engineering, and analytical testing methodologies.
Advanced SQL, Python, statistical analysis, and model development/testing frameworks.
Ability to develop and tune rules-based, statistical, and AI/ML detection models.
Experience with ATL/BTL testing, back-testing, sensitivity analysis, and detection efficacy assessment.
Ability to analyze large datasets, engineer and model features, and diagnose model performance issues.
Hands-on ability to configure, tune, test, troubleshoot, and optimize Fiserv FCRM, LexisNexis Bridger, and comparable financial crimes tools.
Ability to communicate complex analytical findings to technical, business, and governance stakeholders.
Ability to independently lead complex model initiatives without direct people-management authority.
Ability to exercise sound judgment concerning detection effectiveness, operational impact, and model risk.

Nice To Haves

Relevant data science, machine learning, cloud, or financial crimes certifications.

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 or 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.
Balance hands-on model development with technical leadership and cross-domain priorities.
Translate emerging financial crime risks into measurable detection strategies and model enhancements.
Drive consistent testing standards, continuous tuning, and knowledge sharing across domains.