Data Science Manager (Fraud)

Join Plaid, a leading fintech company, as a Data Science Manager in the Fraud Data team. In this role, you will lead a team focused on customer-facing data science and fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will also define product metrics, establish a repeatable process for customer retrospectives and proofs of concept, and identify fraud signals and product opportunities. This is a hands-on role that requires strong technical depth in Python, SQL, statistics, product analytics, and applied modeling.

• Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team.
• Define product metrics, their underlying data, and reporting and alerting practices; use the results in roadmap and investment decisions.
• Establish a repeatable process for customer retrospectives and proofs of concept, including data checks, evaluation methods, and clear recommendations.

• Demonstrated ability to set technical direction and deliver complex, high-impact initiatives through a team while remaining hands-on
• Excellent communication and cross-functional collaboration skills across Product, Engineering, Machine Learning, GTM, and customer stakeholders
• Strong experience in product analytics, metric design, and measuring product performance
• Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling
• Experience partnering directly with customers to deliver data-driven insights and solutions
• Proven experience managing, mentoring, and developing high-performing data scientists
• Deep domain expertise in fraud, risk, or related areas
• We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
• Experience working with graph-based data or systems to identify fraud patterns and improve model performance
• Experience applying causal inference techniques to complex product or risk problems
• Experience using model interpretability techniques across both traditional machine learning and modern model architectures
• Experience with dbt or similar data transformation and analytics engineering tools