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Plaid
New York, United States; Seattle, Washington, United States; San Francisco, California, United States
Source: Plaid careers · View original posting
From Plaid's posting. “We” and “our” refer to the employer.
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.
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