Data Scientist - Fraud
Plaid
- Pay
- $176k–$227k
- Workplace
- Hybrid
- Location
- New York City Office · New York · United States
- First seen
- 1 hour ago
- Last seen
- 1 hour ago
- Board
- Ashby
Summary
We believe that the way people interact with their finances will drastically improve in the next few years.
- python
- sql
Posting
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. We are the Data team within Plaid’s Fraud organization. We build the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s network data to help identify and prevent fraud before it happens. Our team owns the end-to-end ML lifecycle, from feature pipelines and model training to production serving and monitoring, ensuring our systems are reliable, scalable, and built to support hundreds of customers and data partners. As a Data Scientist on the Fraud Data team, you will analyze customer and network traffic to understand how Plaid Protect performs across a range of use cases and customer segments. You’ll build dashboards and metrics that provide a clear, shared view of product performance, run backtests to evaluate performance and identify high-impact rules and model strategies, and generate insights that support customer growth and expansion. You’ll also design scalable data models and schemas to enable reliable analysis and reporting, while partnering closely with Product and Engineering to design and analyze experiments for new customer-facing features. Responsibilities: - Work at the intersection of product analytics, machine learning, and fraud and risk to uncover insights that improve product performance. - Own the metrics, dashboards, and experiments that inform product strategy and decision-making. Qualifications: - 3–5 years of relevant experience, including at least 2–3 years working extensively with product analytics, experimentation, or data-driven products. - Strong proficiency in SQL and Python, with experience analyzing complex datasets and translating insights into action. - Hands-on experience with product an