Financial Crimes Model Analyst Job Tulsa area,Oklahoma USA,IT/Tech

Financial Crimes Model Analyst

SLC
- Salt Lake City, OK; OKC-Gaillardia
- Oklahoma City, OK; SF
- Sioux Falls, SD;
Tulsa-Yale
- Tulsa, OK

Overview

Position Type:
Full Time Job Shift: Day Education Level: 4 Year Degree Travel Percentage:
Negligible Category:
Banking

Description

The Financial Crimes Model Analyst plays a critical role in designing, validating, and optimizing analytical models that support financial crime prevention, including fraud, AML/KYC, and sanctions monitoring. This role leverages modern data-agnostic, low/no-code analytical platforms and machine-learning tooling to operationalize robust detection logic, integrate LLM/SLM outputs responsibly, and ensure alignment with Model Risk Management (MRM) standards.

Principal Duties and Responsibilities

Designs, validates, and enhances financial crime detection models across fraud, AML/KYC screening, and related domains.

Applies statistical techniques to evaluate and calibrate LLM/SLM outputs when used in decision-support workflows.

Conducts ablation studies, back testing, and reproducible experiments to ensure model stability and business impact.

Optimizes model and pipeline efficiency, including latency, throughput, and computational performance.

Develops, tracks, and interprets performance metrics.

Implements monitoring for drift, bias, degradation, and shifts.

Produces audit-ready MRM documentation, including validation plans, governance artifacts, explainability notes, and calibration reports.

Maintains model health dashboards and reporting for executives, governance bodies, and oversight functions.

Partners with internal and external data engineers and vendor partners to maintain reliable inputs and production workflows.

Develops SQL and Python assets for exploration, experimentation, reporting, and automated artifact generation.

Maintains clear and comprehensive documentation, including data dictionaries, model cards, decision logic, and workflow diagrams.

Works cross-functionally with engineering, product, compliance, risk, and vendor teams to deploy and maintain models.

Translates complex analytical concepts into accessible insights for varied technical and business audiences.

Non
- Essential Functions:

Performs other duties as assigned.

Qualifications

Education and/or Experience

Bachelor's degree in Statistics, Econometrics, Data Science, Mathematics, Computer Science, or a related quantitative field, required;
Master's degree, preferred.
3-5 years' experience in model development, evaluation, monitoring, risk governance, or model lifecycle management supporting BSA/AML compliance, fraud and/or case investigation, or experience in quality assurance/control or internal audit, required.
Hands-on expertise in SQL and Python for analysis and rapid prototyping, required.
Familiarity with modern modeling techniques, or similar low/no-code platforms, and the responsible use of LLM/SLM capabilities, required.
Experience producing governance-grade validation or audit documentation, required.
Exposure to financial crime domains (AML, KYC, fraud), preferred.
CAMS and/or CAFP certifications, preferred.

Knowledge, Skills, and Abilities

Strong understanding of supervised/unsupervised ML evaluation, calibration, drift detection, and explainability methods.
Knowledge of data quality domains and data lineage principles.
Familiarity with AI model governance concepts, including MRM standards and regulatory expectations.
Ability to develop and maintain dashboards and report for model health and risk indicators.
Knowledge of regulatory environment(s) and emerging BSA/AML and fraud trends.
Strong investigative, written, and oral communication skills.
Thorough and detail-oriented.
Strong commitment to ethics, and the ability to understand a variety of issues and perspectives.
Understanding of the banking industry, including bank partnerships with fintech companies.
Multitasks effectively and takes action promptly, both independently and in a team environment.
Handles highly confidential information with appropriate discretion, and works well in a high volume, fast paced environment.