Data Scientist - Fraud

Plaid

🔍 Observed

$162K - $222K USD

San Francisco First seen 3 weeks, 1 day ago Last seen 1 hour, 47 minutes ago Lever

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Job Description

AI Summary

• Build and maintain dashboards, metrics, and experimentation frameworks to evaluate fraud detection product performance across different customer segments and use cases. • Analyze network traffic data and run backtests to identify model/rule improvements, uncover expansion opportunities, and support sales efforts. • Design data models and schemas that enable efficient analysis, reporting, and reliable scaling across hundreds of customers. • Partner with Product, Engineering, and go-to-market teams to design experiments, communicate product value to customers, and inform fraud prevention feature development. • Requires 3–5 years of experience with 2–3 years in product analytics, experimentation, or data-driven products; proficiency in SQL and Python; remote work available.

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, Washington D.C., London and Amsterdam.

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.

***We are open to remote candidates***

Responsibilities: 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. Qualifications: 3–5 years of total experience, including at least 2–3 years working deeply with product analytics, 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. Background in fraud or risk domains — Nice to have. 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.

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Job Information

Company:
Plaid
Location:
San Francisco
Job Type:
Full-Time
Experience Level:
Senior
Source:
Lever
Status:
Active

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68 /100

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