Head of Compliance (San Francisco, CA)

Head of Compliance – Data Science

Position Overview

The Head of Compliance – Data Science is a senior leadership role responsible for defining and executing data science strategy for compliance-focused products, with an emphasis on anti-money laundering (AML), sanctions screening, watchlist screening, and compliance risk modeling. This role combines deep technical expertise with strategic leadership to develop scalable machine learning (ML) systems that strengthen risk detection, improve screening accuracy, and support regulatory compliance.

Working closely with Product, Engineering, Compliance, and external stakeholders, the Head of Compliance will oversee technical architecture, establish development roadmaps, drive innovation, and lead a high-performing data science team. The ideal candidate brings extensive compliance domain knowledge, strong ML systems expertise, and the ability to translate complex technical decisions into measurable business outcomes.

Key Responsibilities

Compliance Data Science Strategy

• Define and execute the data science strategy for compliance products, aligning technical initiatives with business objectives and regulatory requirements.
• Establish priorities and long-term roadmaps for machine learning capabilities supporting AML, sanctions screening, watchlist screening, and compliance risk modeling.
• Identify opportunities to improve compliance effectiveness through advanced analytics, automation, and intelligent risk detection.
• Evaluate emerging technologies, modeling techniques, and industry developments to support continued innovation.
• Translate business and compliance needs into actionable data science initiatives with measurable outcomes.

Machine Learning Systems and Architecture

• Oversee the design, development, deployment, and ongoing optimization of scalable ML systems for compliance applications.
• Establish technical architecture and standards for model development, evaluation, deployment, monitoring, and maintenance.
• Guide the development of models and systems that support sanctions screening, watchlist matching, suspicious activity detection, and AML risk assessment.
• Promote reliable, explainable, and maintainable ML solutions appropriate for high-stakes compliance environments.
• Establish practices for model performance monitoring, validation, testing, and continuous improvement.
• Address technical challenges related to scalability, data quality, system performance, and model effectiveness.

AML, Sanctions, and Screening Solutions

• Provide strategic and technical leadership for data science initiatives focused on AML and financial crime risk management.
• Guide the development and refinement of sanctions and watchlist screening models to improve detection accuracy and reduce false positives.
• Oversee analytical approaches for identifying suspicious patterns, assessing risk, and prioritizing alerts for investigation.
• Collaborate with compliance subject matter experts to incorporate regulatory requirements, operational workflows, and risk indicators into model design.
• Ensure data science solutions support appropriate risk-based decision-making, auditability, and compliance oversight.
• Monitor evolving financial crime risks and screening requirements to inform model enhancements and product development.

Technical Leadership and Innovation

• Own technical roadmap prioritization and guide architectural decisions across compliance data science initiatives.
• Drive innovation through advanced machine learning, statistical modeling, and data-driven problem-solving.
• Establish best practices for model development, experimentation, deployment, and performance evaluation.
• Evaluate emerging ML methodologies and determine their suitability for compliance use cases.
• Balance technical innovation with reliability, operational requirements, scalability, and business impact.
• Promote a culture of technical excellence, experimentation, and continuous learning.

Team Leadership and Development

• Lead, mentor, and develop a data science team responsible for compliance-related products and capabilities.
• Establish team objectives, technical standards, and performance expectations.
• Provide guidance on complex modeling challenges, system architecture, and technical decision-making.
• Foster collaboration among data scientists, engineers, product managers, and compliance professionals.
• Support recruitment, professional development, knowledge sharing, and succession planning.
• Build a high-performing team capable of delivering reliable, scalable, and business-focused solutions.

Cross-Functional Collaboration and Stakeholder Management

• Partner with Product and Engineering leaders to translate business requirements into technical solutions.
• Collaborate with Compliance, Risk, and other relevant teams to ensure data science initiatives address operational and regulatory needs.
• Communicate technical strategies, project priorities, risks, and outcomes to executive leadership and other stakeholders.
• Engage external partners and stakeholders to support product development, technical integration, and compliance initiatives.
• Align data science investments with organizational priorities and demonstrate their impact on business performance.
• Build strong relationships across functions to facilitate decision-making and successful project delivery.

Governance, Risk, and Performance Management

• Establish appropriate governance practices for the development and use of ML models in compliance applications.
• Promote model transparency, explainability, documentation, and ongoing performance monitoring.
• Identify and address risks associated with model performance, data integrity, system reliability, and regulatory expectations.
• Define success metrics for data science initiatives and monitor progress against established objectives.
• Ensure technical decisions support responsible, consistent, and effective compliance risk management.
• Drive continuous improvement in model accuracy, operational efficiency, and overall product performance.

Qualifications

Education and Experience

• Bachelor's or advanced degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related technical field.
• Significant experience in data science, machine learning, or applied analytics, including technical leadership responsibilities.
• Strong domain expertise in compliance, particularly sanctions screening, watchlist screening, and financial crime risk management.
• Demonstrated experience with AML analytics and compliance risk modeling.
• Proven experience designing and delivering scalable machine learning systems in production environments.
• Experience leading and developing data science teams and establishing technical roadmaps.
• Background working with compliance, risk, financial services, fintech, or related regulated industries preferred.

Technical Skills

• Strong understanding of machine learning methodologies, statistical modeling, and predictive analytics.
• Experience with ML system architecture, production deployment, model monitoring, and performance optimization.
• Knowledge of sanctions screening, watchlist matching, AML detection, and compliance risk assessment methodologies.
• Ability to evaluate model performance, manage technical trade-offs, and guide architecture decisions.
• Familiarity with data engineering practices, large-scale data processing, and production ML infrastructure.
• Understanding of model governance, explainability, validation, and monitoring in regulated environments.

Core Competencies

• Strategic thinking and the ability to connect technical initiatives to business outcomes.
• Strong executive communication and stakeholder management skills.
• Demonstrated leadership, mentoring, and team development capabilities.
• Excellent analytical, problem-solving, and decision-making skills.
• Ability to balance innovation with regulatory, operational, and business requirements.
• Strong cross-functional collaboration and relationship-building abilities.
• Comfortable navigating complex technical challenges and evolving compliance requirements.
• Ability to communicate sophisticated technical concepts clearly to technical and nontechnical audiences.