Fraud Strategy Lead: ROI & Retro Programs

Plaid Inc; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity

Commission

Health benefits

Dental

Vision

401(k)

Job summary

Plaid is hiring a Fraud Strategist to bridge data science and business value, owning retro and POC programs and driving adoption of our Protect fraud products. You’ll translate model outputs into a clear ROI for customers and coordinate with internal stakeholders.

Ideal candidates have 5–10 years in a customer-facing analytical role in fintech, experience with data science teams, and the ability to explain ML results to non-technical audiences.

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.

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.

Skills

Customer analytics

Stakeholder management

Data interpretation

ML interpretation

Job description

Plaid is hiring a Fraud Strategist to bridge data science and business value, owning retro and POC programs and driving adoption of our Protect fraud products. You’ll translate model outputs into a clear ROI for customers and coordinate with internal stakeholders.

Ideal candidates have 5–10 years in a customer-facing analytical role in fintech, experience with data science teams, and the ability to explain ML results to non-technical audiences.