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Senior Data Scientist - Fraud

United States, Seattle 214800.00 - 286800.00 USD / Year · Job Posted April 17, 2026
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Job Description

The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s newest fraud detection products, leveraging the breadth of Plaid’s network data to stop fraud before it occurs. We manage the full lifecycle of these systems—from feature pipeline development and model training to deployment, serving, and monitoring—ensuring our solutions scale reliably as we grow to support hundreds of customers. As a Data Scientist on Plaid’s Fraud Data team, you will analyze customer and network traffic to understand how Protect performs across different segments and use cases. You’ll build dashboards and performance metrics that create a clear, shared view of product health for both the team and our go-to-market partners. You will 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. You’ll also design the underlying data models and schemas that enable efficient, reliable analysis and reporting. In close partnership with Product and Engineering, you will help design and evaluate experiments that shape new customer-facing features and inform the future of our fraud products.

Job Responsibility

  • 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
  • Partner with go-to-market teams to demonstrate the technical and business value of our products to customers

Requirements

  • 5+ years of total experience
  • At least 2–3 years working deeply with product analytics, machine learning, experimentation, or data-driven products
  • 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
  • Excellent communicator with strong stakeholder-management skills across diverse teams

Nice to have

  • Background in fraud or risk domains
  • Familiarity with data-insights products and a solid understanding of model-performance metrics
  • Exposure to customer-facing or GTM-facing analytics

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