Data Science Manager - Fraud

The role

You will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team roadmap, develop data scientists, and remain involved in analytical methods, technical reviews, and customer investigations.

What you will do

• 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.
• Set a 6–12-month roadmap with Product, Engineering, and GTM.
• Define product metrics, their underlying data, and reporting and alerting practices.
• Establish a repeatable process for customer retrospectives and proofs of concept.
• Identify fraud signals and product opportunities.
• Review analytical designs, data models, code, and model evaluations.

Required qualifications

• Proven experience managing, mentoring, and developing high-performing data scientists.
• Deep domain expertise in fraud, risk, or related areas.
• Strong experience in product analytics, metric design, and measuring product performance.
• Experience partnering directly with customers to deliver data-driven insights and solutions.
• Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling.
• 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.

Nice to have

• 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.

Benefits

• Medical
• Dental
• Vision
• 401(k)

About the company

Plaid powers the tools millions of people rely on to live a healthier financial life. The company works with thousands of companies to make it easy for people to connect their financial accounts to apps and services.