Data Scientist - Fraud

Plaid; New York (NY); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Equity

Benefits package

Job summary

Plaid is a leading fintech data platform enabling developers to connect with financial institutions. We are looking for a Data Scientist for the Fraud Data team to analyze traffic, build dashboards, run backtests, and generate insights that drive product performance and growth.

The role emphasizes end-to-end ML lifecycle, collaboration with Product and Engineering, and designing scalable data models to support hundreds of customers and data partners. Equity/incentives are part of compensation.

Qualifications

• 3–5 years of relevant experience, including 2–3 years in product analytics, experimentation, or data-driven products.
• Strong proficiency in SQL and Python with the ability to translate analyses into action.
• Hands-on experience with dashboards, reporting frameworks, and core product metrics.
• Excellent communication and stakeholder management to explain insights to diverse audiences.

Responsibilities

• Work at the intersection of product analytics, machine learning, and fraud to uncover insights that improve product performance.
• Own metrics, dashboards, and experiments informing product strategy and decision-making.

Skills

SQL

Python

Product analytics

Experimentation

Dashboards

Job description

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 analytics, experimentation, and/or backtesting methodologies.

• Experience building and maintaining dashboards, reporting frameworks, and core product metrics.

• Strong communication and stakeholder management skills, with the ability to translate complex analyses into clear, actionable insights for technical and non‑technical audiences.

Nice-to-Have:

• Experience in fraud/risk domains

• Experience with developing ML models

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!

Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [e-mail].

Please review our Candidate Privacy Notice here.

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.