Job Description
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Company Overview
A leading financial technology company is helping some of the world’s best-known brands grow customer lifetime value through modern co-branded credit card programs. The organization combines advanced payments infrastructure, intelligent underwriting, and customer data to create personalized member experiences while enabling brand partners to offer powerful financial products without becoming banks.
Key Responsibilities
- Own and improve onboarding fraud decisioning across the application journey, including identity verification, KYC controls, application fraud models, policy rules, decline and verification waterfalls, and manual-review strategies.
- Build, validate, deploy, and monitor models that detect identity theft, synthetic identity, first-party fraud, and coordinated application abuse.
- Leverage identity, device, behavioral, application, bureau, network, and consortium signals to improve fraud detection.
- Evaluate third-party fraud and identity vendors by measuring scores, attributes, incremental lift, overlap, coverage, stability, latency, and cost.
- Recommend when fraud and identity signals should be added, replaced, or retired based on analytical performance and business value.
- Design and analyze A/B tests, shadow tests, holdouts, and champion/challenger strategies.
- Balance fraud losses and fraud capture against approval rates, false positives, verification friction, and manual-review volume.
- Investigate emerging fraud patterns and decision misses using application data, post-booking outcomes, and feedback from Fraud Operations.
- Develop new features, rules, models, and review strategies based on fraud investigations and emerging patterns.
- Build monitoring and AI-powered workflows to detect model drift, population shifts, vendor degradation, data-quality issues, and new attack patterns.
- Recommend adjustments to fraud decisioning and human-review processes based on analytical findings.
- Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy teams to productionize changes and validate business impact.
- Communicate analytical findings, recommendations, and decision tradeoffs to senior leadership and external partners.
Required Qualifications
- 5–8+ years of experience in data science, risk analytics, or a related quantitative field, preferably within a high-growth startup or fintech environment.
- Strong Python and SQL skills, including the ability to build models, transform raw data, and create custom datasets from complex financial data.
- Experience building and evaluating predictive models for fraud, identity, KYC, AML, credit risk, trust and safety, or other adversarial classification problems.
- Strong understanding of supervised machine learning, model validation, backtesting, calibration, feature engineering, and production model monitoring.
- Deep understanding of statistical inference and experiment design, including A/B testing, holdouts, champion/challenger testing, causal measurement, and tradeoff analysis.
- Ability to evaluate complete decision systems using metrics such as fraud capture, loss rate, false-positive rate, approval impact, verification friction, operational workload, and economic value.
- Strong full-stack problem-solving skills, with the ability to trace decisions from raw inputs and vendor responses through model scores, policy rules, and downstream outcomes.
- Experience owning analytical projects end-to-end, from problem definition and exploratory analysis through production implementation, monitoring, and business impact measurement.
- Ability to communicate complex analytical findings and decision tradeoffs clearly to both technical and non-technical audiences.
- Experience using AI tools to accelerate analysis, investigation, feature development, documentation, and monitoring.
- Interest in developing AI-powered risk systems and analytical workflows.
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