Modeling Specialist, Payment Risk
Plaid
- Location
- San Francisco HQ
- Employment
- Full Time
- Work model
- Remote
- Level
- Staff
- H-1B history
- 9 approvals (FY2023)
- Posted
- 2h ago
Skills
About this role
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. Our Payments Risk team builds and operates decisioning capabilities across Signal and Guarantee. We help customers tune thresholds and rules so they can approve more good transactions while reducing returns. For Guarantee, this work also protects the economics of the risk Plaid underwrites. You will be the technical, customer-facing modeling specialist for Signal and Guarantee. You will interpret model outputs, diagnose customer performance, and turn that analysis into concrete threshold and rules recommendations. You will own proofs of concept and retros, improve existing integrations, and help the team identify patterns that can be scaled through better processes and product capabilities.
Responsibilities
Own customer proofs of concept and retros from analysis through recommendations and follow-through. Proactively optimize Signal customers, prioritizing accounts with high return rates. Read model outputs and diagnose the drivers of authorization and return-rate performance. Recommend threshold and rules changes that align with each customer's risk and authorization goals. Help Guarantee customers tune thresholds to achieve target authorization rates while protecting loss performance. Partner with AEs and TAMs to bring deep product and modeling expertise into customer engagements. Improve decision and return reporting from customers to strengthen their experience and Plaid's models. Turn recurring customer insights into onboarding playbooks and product requirements.
Qualifications
5–10 years of experience Comfortable pulling and cutting your own data (SQL) Experience interpreting risk, decisioning, or other quantitative model outputs. Experience diagnosing performance problems and making data-backed threshold or rules recommendations. Strong customer-facing consulting and communication skills. Experience partnering across go-to-market and technical teams. Demonstrated ability to build repeatable processes in an ambiguous environment. [Nice to have] Experience at a fraud or risk-scoring vendor, credit bureau, or bank / processor risk team. [Nice to have] Familiarity with cash flow or bank transaction data. 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