Leads fraud model retro-as-a-service and POC programs, partnering with customers to demonstrate ROI of fraud products and drive adoption. Collaborates between data teams and customers, interpreting ML outputs for technical and non-technical audiences; requires 5-10 years in customer-facing fintech analytics.
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About the role
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
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
Skills
Machine LearningData ScienceFraud DetectionRisk ManagementPrecision/RecallMl Model InterpretationFintechConsultative SalesProof Of ConceptROI Analysis
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