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.
Our Fraud team's mission is to help companies detect and prevent fraud using financial network data. We believe that transaction patterns, device signals, and behavioral data are an underleveraged tool in fraud prevention.
The Fraud Consulting Lead is responsible for building and maturing our retro-as-a-service and POC program and driving adoption of our fraud products. The role sits between our data team and the customer (i.e., the role will not build the models, but will need to understand the output well enough to clearly and compellingly present the business case and work with technical stakeholders and customers.)
Responsibilities:
Own the retro and POC process end-to-end and collaborate with customers and internal stakeholders at Plaid
Partner closely with customers to help them understand the ROI of Protect and make recommendations for implementing our fraud products
Drive post-retro follow-through to convert retro results into production usage
Serve as a feedback loop between customers and product to drive our Protect roadmap
Qualifications:
5-10 years of experience in a customer-facing analytical role in fintech, financial services, or a related domain (e.g., software/tech)
Experience working closely with data science teams
Experience managing consultative customer engagements where you owned the deliverable and the relationship
Comfort interpreting ML model output (e.g., precision/recall, score distributions) and explaining it to a non-technical audience
[Nice to have] Background in data science or ML engineering, particularly on a fraud or risk team
[Nice to have] Experience in fraud, identity, or risk — either from a fraud prevention vendor, a fraud team at a bank or fintech, or consulting on risk engagements
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 [email protected].
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.
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