Compliance Technology Program Lead

Block•New York, NY

2d•$142,600 - $257,600•Remote

About The Position

Block is seeking a Compliance Technology Program Lead to ensure our Machine Learning and Heuristic detection models — and the data infrastructure that feeds them — are fit for purpose, for our fiat and virtual currency products. This role sits at the intersection of compliance, machine learning, and AI tooling: you will use modern intelligence tools, including Claude and other LLM-based agents, to automate significant portions of oversight, monitoring, and documentation work — turning a traditionally manual, documentation-heavy function into a scalable, defensible program. You will partner with Machine Learning, Engineering and BSA/AML Policy Teams to provide end-to-end technical oversight, effective challenge and ongoing monitoring throughout the model development lifecycle.

Requirements

7+ years of experience in compliance technology at regulated financial institutions, or as a consultant working with FIs.
Hands-on experience building with modern AI tools — including Claude, GPT, or comparable LLM-based agents — and a track record of using them to automate work in production. This includes prompt engineering, designing agentic workflows, and integrating AI into operational processes.
Experience with both Machine Learning and Heuristic models across Transaction Monitoring, Sanctions Screening, and Customer Risk Scoring.
Experience with input data quality monitoring.
Experience with model performance monitoring.
Experience conducting AML model validations aligned with SR 11-7 and OCC 2011-12.
Experience responding to examinations and consent orders, and working with regulators and independent monitors.
Experience with virtual currencies, including the unique transaction monitoring and sanctions screening considerations they present.
Familiarity with blockchain analytics and screening tools such as Chainalysis and TRM Labs.
A track record of driving cross-functional initiatives.
A working understanding of regulated financial services and corresponding compliance obligations.
Familiarity with statistical concepts related to machine learning
Expertise in data analytics and visualization

Responsibilities

Build automation using AI agents (Claude and similar LLM-based tools) to scale oversight, monitoring, and documentation work, including drafting requirements, exam-ready documentation, data quality monitoring, and ongoing model performance reporting.
Design human-in-the-loop checkpoints to ensure automated outputs remain defensible to regulators and independent monitors.
Establish and maintain model and data governance frameworks aligned with SR 11-7 and OCC 2011-12, including a revamped Detection Requirements Document (DRD) process.
Provide effective challenge throughout the model development lifecycle and partner with BSA/AML Policy and CAX to ensure documentation is exam-ready.
Lead the design and development of real-time dashboards to monitor model health, including input data monitoring and ongoing model performance.
Set standards for threshold tuning and ongoing Below the Line (BTL) testing controls.
Establish frameworks and triggers for identifying when model performance is degrading and requires tuning or deprecation.
Partner during subsequent tuning exercises and ensure changes are within risk appetite and documented defensibly.
Oversee Transaction Monitoring models, including heuristic and rules-based detection logic, threshold settings (below the line testing), and the data pipelines that feed alerts.
Oversee blockchain analytics and screening tools (TRM, Chainalysis), with attention to typology coverage, alert quality, and integration with downstream investigations.
Oversee Sanctions Screening models (LexisNexis), including list management, fuzzy-match tuning, and false-positive reduction.
Oversee Customer Risk Scoring models, including segmentation logic, periodic recalibration, and effective challenge of risk drivers.
Oversee the design and validation of AI-driven case resolution — LLM-based and ML-based models that triage, enrich, or disposition alerts — ensuring decisions are explainable, auditable, and defensible to regulators.