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Signifyd is a fraud-prevention and ecommerce trust platform that processes billions of transactions annually for thousands of merchants globally. The company uses advanced AI and machine learning to help online retailers approve more legitimate orders while protecting against fraud and abuse.
You will lead a team of engineers in the modeling group, which creates, deploys, and operates the ML models and services that power Signifyd's fraud detection capabilities. This is a people-management role with significant technical engagement—you'll partner closely with data scientists, ML engineers, product owners, and risk management stakeholders to bring innovations from conception to production while maintaining the reliability of existing systems.
Key responsibilities include:
**Team Leadership & Growth**: Manage and coach a team of engineers, providing feedback and career development. Build a healthy team culture with clear ownership, sustainable pace, and high quality standards. Recruit, onboard, and develop technical leaders.
**Stakeholder Management**: Balance competing priorities across multiple groups (AI lab, product, risk management, broader engineering). Represent your team's roadmap, capacity, and risks in cross-functional planning. Build trust through transparency and consistent delivery.
**Technical Engagement**: Drive technical discussions and design reviews with enough depth to understand implications for reliability, scalability, and production. Partner with engineers on architecture decisions. Ensure technical choices are well-understood and defensible to stakeholders.
**AI-Enabled Ways of Working**: Model and champion effective use of AI tools across the team's daily work—research, administrative workflows, code generation. Help the team develop good judgment about balancing productivity gains against risks. Continuously identify opportunities for AI tooling to improve efficiency and code quality.
**Delivery & Operations**: Own delivery of the team's roadmap, balancing feature work, technical debt, and reliability commitments. Establish quality, testing, and monitoring practices. Provide clear, proactive communication on progress, risks, and blockers. Define and track metrics.
You should be comfortable operating on both the technical and business sides of the line, acting as a trusted translator between engineering, AI, product, and risk management. Success requires strong people leadership, technical credibility, and the ability to drive closure on complex decisions.
