Note: The job is a remote job and is open to candidates in USA. Imprint builds co-branded credit card programs and financial products using payments infrastructure, intelligent underwriting, and customer data. The Data Scientist, Fraud Risk will own onboarding fraud modeling and analytics from application submission through account opening, developing decision systems that detect fraud while minimizing false positives and applicant friction. The role also involves evaluating vendors, designing experiments, monitoring fraud systems, and partnering with cross-functional teams to implement scalable risk controls.
Responsibilities
- Own and improve Imprint's onboarding fraud decisioning across the full 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 using identity, device, behavioral, application, bureau, network, and consortium signals
- Evaluate third-party fraud and identity vendors by testing scores and attributes, measuring incremental lift, overlap, coverage, stability, latency, and cost, and recommending when to add, replace, or retire signals
- Design and analyze A/B tests, shadow tests, holdouts, and champion/challenger strategies, balancing fraud losses and capture against approval rate, false positives, verification friction, and manual-review volume
- Investigate emerging fraud patterns and decision misses, combining application and post-booking outcomes with Fraud Operations feedback to develop new features, rules, models, and review strategies
- Build monitoring and AI-powered workflows that detect model drift, population shifts, vendor degradation, data-quality issues, and new attack patterns—and recommend adjustments for human review
- Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize changes, validate their impact, and communicate recommendations to senior leadership and external partners
Skills
- 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field, ideally at a high-growth startup or fintech company
- Strong Python and SQL skills, with 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 another adversarial classification problem
- 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 tests, holdouts, champion/challenger tests, causal measurement, and tradeoff analysis
- Ability to evaluate decision systems—not just model performance—using metrics such as fraud capture, loss rate, false-positive rate, approval impact, verification friction, operational workload, and economic value
- Full-stack problem-solving orientation: you can trace a decision through raw inputs, vendor responses, model scores, policy rules, and downstream outcomes to find the root cause of a problem
- Comfort owning 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 technical and non-technical audiences
- Comfort using AI tools to accelerate analysis, investigation, feature development, documentation, and monitoring—and excitement about building AI-powered risk systems
- Experience with application or onboarding fraud, including identity theft, synthetic identity, first-party fraud, application manipulation, or fraud rings
- Familiarity with KYC, CIP, identity verification, document verification, device intelligence, behavioral signals, consortium data, credit bureau data, or alternative data sources
- Experience evaluating and integrating third-party fraud or identity vendors, including measuring incremental value relative to existing controls
- Experience with real-time scoring, decision engines, rules platforms, APIs, or production ML systems
- Experience partnering with fraud operations or investigations teams and converting case-review findings into scalable controls
- Familiarity with credit card underwriting, consumer lending, or regulated financial products
- Experience with graph, anomaly-detection, or weakly supervised methods for identifying coordinated or emerging fraud patterns
Benefits
- Equity packages
- Leading configured work computers of your choice
- Flexible paid time off
- Fully covered, high-quality healthcare, including fully covered dependent coverage
- Access to One Medical and the option to enroll in an FSA
- 20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents
- Access to industry-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity
Company Overview
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