Plaid's Fraud Data team builds machine learning systems to detect and prevent fraud in financial transactions, aiming to protect customers and the platform.
Requirements
Strong proficiency in SQL and Python.
Hands-on experience with product analytics, experimentation frameworks, or backtesting methodologies.
Skilled in designing, building, and maintaining dashboards and core product performance metrics.
Capable of designing and interpreting backtests or offline evaluations for ML and rules-based systems.
Familiarity with data-insights products and a solid understanding of model-performance metrics — Nice to have.
Exposure to customer-facing or GTM-facing analytics — Nice to have.
Responsibilities
Build dashboards and performance metrics that create a clear, shared view of product health for both the team and our go-to-market partners.
Run backtests on customer traffic to evaluate model and rule performance, uncover high-value opportunities, and generate insights that support sales motions and customer expansion.
Design the underlying data models and schemas that enable efficient, reliable analysis and reporting.
Help design and evaluate experiments that shape new customer-facing features and inform the future of our fraud products.
Work at the intersection of product analytics, machine learning, and fraud/risk to drive meaningful product improvements.
Own the metrics, dashboards, and experimentation frameworks that inform product strategy and decision-making.
Analyze complex datasets to uncover clear, actionable insights that shape product direction.
Other
3–5 years of total experience, including at least 2–3 years working deeply with product analytics, experimentation, or data-driven products.
Excellent communicator with strong stakeholder-management skills across diverse teams.
Background in fraud or risk domains — Nice to have.
We are open to remote candidates
We encourage you to apply to a role even if your experience doesn't fully match the job description.
