Data Science Manager (Fraud)

• Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products
• Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions
• The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities
• We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio
• As a Data Science Manager, you will lead a team responsible for 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:
• 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
• Identify fraud signals and product opportunities that recur across customer analyses and work with Product and MLEs to develop them
• Review analytical designs, data models, code, and model evaluations; contribute directly to investigations where your expertise is needed
• Coach data scientists through clear expectations, regular feedback, performance discussions, and growth opportunities
• Use AI-assisted analysis and development tools where useful, and ensure results are properly reviewed before informing customer recommendations or product decisions
• Define how Plaid measures, evaluates, and improves the performance of its Fraud products
• Apply fraud expertise, product analytics, and customer-facing data science to drive end-to-end product and business impact
• Translate customer insights and fraud analyses into scalable product capabilities and opportunities for GTM growth
• Lead and develop a high-performing team while remaining technically hands-on with critical analyses and initiatives
• Raise the bar for product metrics, analytical rigor, and the data foundations that power decision-making across Fraud

Benefits

• Vibrant offices in SF, NYC, and Raleigh-Durham—with catered meals, happy hours, and clubs to keep you connected
• Competitive pay, comprehensive health benefits, and support for fertility, mental health, and parental leave
• Lifestyle perks including home office stipends, daycare support, and commuting benefits like CitiBike and Lyft
• 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