Jobs › Senior Data Scientist - (Payment Risk/Fraud) Embedded Insights
Senior Data Scientist - (Payment Risk/Fraud) Embedded Insights
Role at a glance
- Category
- Risk
- Work arrangement
- On-site
- Location
- San Francisco
- Posted
- Jul 3, 2026
Plaid is hiring a Senior Data Scientist - (Payment Risk/Fraud) Embedded Insights in San Francisco. This is a Risk role in the governance, risk, and compliance field. Review the full details below and apply directly with Plaid.
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 Embedded Insights team builds machine learning models to enable better internal decision making and to power the Plaid product suite. We are structured as a central team of MLEs and Data Scientists, and embed with partner teams to bring ML models to life. As the first Data Scientist on Plaid’s Embedded Insights team within the Data organization, you will play a foundational role in building the analytics and measurement framework that supports a broad portfolio of internal and customer-facing products. You will partner closely with product, engineering, and machine learning teams to drive data-informed decision making, evaluate product and model performance, and contribute directly to the health and growth of the Plaid network. In this role, you will analyze entities across the Plaid network to better understand behavior patterns and develop metrics and monitoring systems that identify anomalies and emerging risks. You will create dashboards and reporting frameworks that provide clear visibility into machine learning model performance, while also evaluating the impact and value of these models on both customer and internal datasets. A key part of your work will involve translating complex analyses into compelling, actionable narratives for technical and business stakeholders. You will design and analyze experiments, communicate findings across teams, and use data-driven insights to uncover opportunities to improve existing products and expand Plaid’s offerings. Responsibilities: Applying your expertise in quantitative analysis, data mining, and data visualization to keep the Plaid network safe and improve our product suite. Informing and influencing product and engineering teams
Full responsibilities and requirements are on Plaid's application page.
Apply for this role →Location and market context
This role is based in San Francisco on-site. Local candidates benefit from being close to Plaid's teams and regional hiring market. Confirm the exact in-office expectation and any relocation support with the employer.
About risk management roles
Risk roles own the methodology for identifying, assessing, and escalating enterprise, operational, and technology risk. Second-line teams set risk appetite and challenge the first line. Roles like this one are typically evaluated against frameworks such as enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices.
How to position yourself for this risk management role
Strong candidates emphasize risk assessment methodology, appetite and escalation, cross-functional partnership, and clear reporting to senior leadership and the board. In your resume and outreach, tie your experience to how Plaid would apply enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices, and lead with concrete outcomes rather than duties.
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