Fraud Strategy Manager / Senior Manager (Onboarding)

Cardless•San Francisco, CA

3d•$150,000 - $210,000•Onsite

About The Position

At Cardless, we’re building a credit card and loyalty platform that consumer businesses use to engage their customers. We’ve launched 14 credit cards, including for Coinbase, Alibaba, and Qatar Airways. We help businesses bring imaginative card programs to life, and have pioneered technology to embed credit card features natively into their products. We value curiosity, humility, and intensity — we move fast and take ownership. This is a place where a motivated, resourceful individual can have an enormous impact on our trajectory. We're headquartered in San Francisco, and have raised about $90M in equity funding from top venture capital firms and angels. The Job We're looking for a Fraud Strategy Manager or Senior Manager – Onboarding to own the end-to-end fraud strategy for how we evaluate new applicants. This is a highly analytical, strategy-forward role focused on building the decisioning framework that determines which applicants are approved, stepped up for review, or declined, and making sure the tools, data, and processes behind those decisions are best-in-class. Reporting to our Chief Risk Officer, you'll work at the intersection of data, tooling, and operations to sharpen our onboarding defenses as Cardless scales in the next year. You'll own the manual review program, drive the strategy for leveraging alternative data signals from bank data, device intelligence, and partner sources, and build the measurement infrastructure to continuously improve performance.

Requirements

5–10 years of experience in fraud strategy, identity risk, or credit risk at a financial institution, fintech, or payments company.
Deep expertise in onboarding fraud — synthetic identities, identity manipulation, first-party fraud vectors, and deposit/funding fraud.
Hands-on experience with bank data providers in a fraud or credit risk context.
Familiarity with device intelligence and behavioral fraud platforms.
Strong SQL skills and experience using data to build and evaluate fraud rules, track performance, and identify emerging patterns.
Excellent communication skills — you can translate complex fraud tradeoffs into clear recommendations for executives, partners, and compliance teams.
Proactive, bias-to-action mindset: you ask the right questions, align stakeholders, and drive decisions forward.

Responsibilities

Define the logic and thresholds for routing applicants into manual review, balancing fraud risk against approval rates and customer experience.
Own the manual review program end-to-end: queue prioritization, SLA design, analyst tooling, and case escalation paths.
Evaluate the effectiveness of tools used in manual review; identify gaps and advocate for new capabilities that help analysts make faster, higher-quality decisions.
Track manual review outcomes rigorously: analyst decisions, approval/decline rates, reversal rates, downstream fraud on reviewed accounts, and false positive costs.
Build structured feedback loops between review outcomes and upstream rules triggers to drive continuous policy refinement.
Lead the strategy for incorporating bank account data into onboarding decisions, leveraging signals such as account tenure, balance history, income patterns, and return/NSF activity.
Operationalize device intelligence and behavioral signals to strengthen identity and fraud detection at the top of the funnel.
Develop a framework for using partner data: loyalty engagement, transaction history, tenure signals as supplementary fraud indicators in co-brand card programs.
Evaluate new data sources and vendors on an ongoing basis; build a rigorous test-and-learn methodology to validate signal lift before production deployment.
Own the fraud rules framework for onboarding: design, test, implement, and continuously tune rules across identity, velocity, device, funding, and behavioral dimensions.
Partner with data science to define feature requirements, evaluate model performance, and translate model outputs into operational policy.
Document all policy decisions clearly, including the tradeoffs made at each threshold.
Define and own the KPI framework for onboarding fraud: fraud rate by vintage and partner, manual review rate, auto-decisioned bad rate, tool efficacy, and cost-per-review.
Conduct regular portfolio reviews to surface emerging fraud patterns, track loss trends, and assess detection performance.
Build reporting to enable real-time monitoring, trend identification, and rapid policy response.
Partner with Product and Engineering to translate fraud strategy into system requirements and influence roadmap prioritization.
Work with Compliance and Legal to ensure onboarding controls meet BSA/AML, Red Flags Rule, ECOA, and UDAAP requirements.
Collaborate with Customer Operations to manage edge cases, decision appeals, and applicant escalations.