Plaid의 Fraud Data PM은 Protect 플랫폼의 데이터 인프라와 신호 스택을 고도화하는 역할을 수행합니다. 실무 수준의 부정 방지(Fraud Prevention) 경험과 데이터 과학자 및 ML 엔지니어와의 긴밀한 협업 능력이 필수입니다. 모델의 요구사항을 제품 요구사항으로 변환하고, 데이터 기반의 의사결정을 통해 금융 서비스의 부정 탐지 성능을 극대화하는 핵심적인 역할을 맡게 됩니다.
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 Fraud team is building the largest network of trusted identities in the US. Our products span the full user lifecycle: Identity Verification and Monitor for onboarding and compliance, Layer for frictionless returning-user experiences, and Protect, our fraud intelligence platform, as the connective risk layer for all of it. Protect has more inbound demand than any product we have launched. The challenge is signal. We are building the data infrastructure that will make Protect the best fraud signal in financial services. This is a product role for someone who lives at the intersection of fraud expertise and data science. You will work directly with our data team to build and improve Protect's signal stack: the attributes, scores, and fraud vector intelligence that customers use to make real-time decisions. You need to understand fraud, know how to partner with data scientists as a peer, and be able to translate between what models need and what products should deliver.
Own the feature roadmap for Protect: what we build, how we validate it, and how we measure coverage and fill rates
Build and manage the labeling pipeline as a product, including customer retros, data partnerships, and the label feedback loop into model training
Define the product requirements for fraud vector scores, including signal selection, precision/recall targets, and customer-facing output design
Partner with data scientists and MLEs as a day-to-day collaborator, not just a requester. Translate between model needs and product requirements in both directions
Work with GTM and Protect PM on how attribute improvements and new scores translate into customer-facing value propositions.
Required:
Real hands-on fraud prevention experience: fraud vectors, detection systems, risk signal evaluation. Not general fintech.
Direct experience collaborating with data scientists or ML engineers to ship data-driven products. You need to be able to talk to a DS about feature distributions, label quality, and model performance, not just read summaries.
Nice to have:
Experience at a fraud vendor (SentiLink, Sardine, Socure, Persona, Incode) or a fraud operations team at a large fintech or bank
Background with ML model inputs/outputs: feature engineering, offline evaluation, and moving from experimentation to production
Specialization in a specific fraud vector (ATO, synthetic identity, or first-party fraud)
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 accommodations@plaid.com.
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.
