Senior Associate, Compliance Model Governance

Design and run quantitative testing for AI and ML models both before and after launch — including input data quality checks, variance and stability testing, and performance testing for agentic and LLM-based systems — producing documentation robust enough for independent model validation and regulatory review.

Build and maintain monitoring pipelines and dashboards that track model inputs, outputs, and downstream results (such as SAR performance metrics), using statistical methods to catch drift or degradation and delivering clear status updates to model owners and Compliance leadership.

Own data lineage for compliance-critical data elements, creating data quality checks that support Compliance's data quality control framework, and working with Engineering to trace data from its source systems through to the controls that consume it.

Write production-quality SQL and Python pipelines, troubleshoot and resolve pipeline issues, and lead data migrations that support external regulatory reporting.

Develop detective monitoring scenarios to test whether compliance controls are functioning as intended, including writing and validating the underlying SQL logic.

Prepare testing evidence, model documentation, and walkthroughs for Internal Audit, model validators, and regulators, and manage remediation of findings through to closure.

4+ years of experience in data science, model risk, quantitative analytics, or data engineering, ideally within financial services, fintech, or another regulated industry.

Solid grounding in applied statistics (hypothesis testing, drift and stability measures, sampling, confidence intervals), with hands-on experience evaluating ML models, including testing LLM-based or agentic systems (e.g., accuracy against human-reviewed samples, variance across runs, input and payload monitoring).

Advanced SQL and Python skills, with experience building monitoring dashboards and working in cloud data environments (e.g., Snowflake or Databricks), including data lineage tracing and data quality rule design.

Demonstrated ability to produce testing and model documentation that holds up under independent review, and to clearly communicate technical findings to non-technical compliance stakeholders and auditors.

Responsible use of generative AI, with appropriate human oversight, to produce business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.