Data Science Manager - Fraud

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

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.

Responsibilities:

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.

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.