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Plaid is seeking a Data Science Manager to lead the Customer & Product Intelligence team within Plaid's Fraud organization. This team is responsible for using data and machine learning to improve and scale Plaid's fraud products, with a focus on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions.
In this role, you will manage a team of data scientists and own the end-to-end strategy for customer-facing data science and fraud product analytics. You will set a 6–12-month roadmap in partnership with Product, Engineering, and GTM teams, defining priorities and responsibilities across your team. You will establish how Plaid measures, evaluates, and improves the performance of its Fraud products, including defining product metrics, their underlying data, and reporting and alerting practices.
Key responsibilities include:
- Setting team roadmap and priorities with cross-functional partners
- Defining product metrics and data foundations that guide investment decisions
- Establishing repeatable processes for customer retrospectives and proofs of concept
- Identifying fraud signals and product opportunities that recur across customer analyses and collaborating with Product and MLEs to develop them
- Reviewing analytical designs, data models, code, and model evaluations; contributing directly to critical investigations
- Coaching and developing data scientists through clear expectations, feedback, and growth opportunities
- Translating customer insights and fraud analyses into scalable product capabilities
- Raising the bar for product metrics, analytical rigor, and data foundations across the Fraud organization
You will remain technically hands-on with critical analyses and initiatives, using AI-assisted tools where useful while ensuring proper review before informing customer recommendations or product decisions.
Requirements:
- 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 qualifications:
- Experience with graph-based data or systems to identify fraud patterns
- Experience applying causal inference techniques to complex product or risk problems
- Experience with model interpretability techniques across traditional ML and modern architectures
- Experience with dbt or similar data transformation and analytics engineering tools
