ML-Driven Financial Crime Modeling Specialist

Stride Bank, N.A.; Sioux Falls (SD); On-site; USD 90,000 - 130,000; Full time

Job summary

Stride Bank, N.A. seeks a Financial Crimes Model Analyst to design, validate, and optimize analytical models that support fraud, AML/KYC, and sanctions monitoring.

The role leverages low/no-code platforms and ML tooling to operationalize detection logic and align with Model Risk Management standards. You will produce audit-ready documentation, monitor model health, and collaborate with data engineers, risk, compliance, and engineering teams to deploy robust models and dashboards, translating

Qualifications

• BS degree in a quantitative field required; MS preferred.
• 3–5 years in model development, evaluation, or governance for financial crime.
• Hands-on SQL and Python for analysis and prototyping.
• Experience with governance-grade validation or audit docs.
• Familiarity with AML, KYC, fraud domains preferred.

Responsibilities

• Designs, validates, and enhances financial crime detection models.
• Applies stats to calibrate LLM/SLM outputs in workflows.
• Conduct ablation studies, back testing, and reproducible experiments.
• Optimize model pipelines for latency and throughput.
• Develops performance metrics and dashboards for risk monitoring.
• Implements drift, bias, and degradation monitoring.
• Produces audit-ready MRM documentation and governance artifacts.
• Collaborates with data engineers, risk, compliance, and vendors.
• Translates complex analytics into accessible business insights.

Skills

SQL

Python

ML evaluation

Model governance

Drift detection

Explainability

Dashboards

Data quality

Education

Bachelor's in Statistics / Data Science / Math / CS

Master's degree preferred

Tools

Low/no-code platforms

Job description

Stride Bank, N.A. seeks a Financial Crimes Model Analyst to design, validate, and optimize analytical models that support fraud, AML/KYC, and sanctions monitoring.

The role leverages low/no-code platforms and ML tooling to operationalize detection logic and align with Model Risk Management standards. You will produce audit-ready documentation, monitor model health, and collaborate with data engineers, risk, compliance, and engineering teams to deploy robust models and dashboards, translating