Data Scientist, Fraud Risk

Own and improve Imprint's onboarding fraud decisioning across the full application journey
Develop identity verification, KYC controls, application fraud models, policy rules, decline and verification waterfalls, and manual-review strategies
Build, validate, deploy, and monitor models detecting identity theft, synthetic identity, first-party fraud, and coordinated application abuse
Evaluate third-party fraud and identity vendors using lift, overlap, coverage, stability, latency, and cost metrics
Design and analyze A/B tests, shadow tests, holdouts, and champion/challenger strategies
Balance fraud losses and capture against approval rate, false positives, verification friction, and manual-review volume
Investigate emerging fraud patterns and decision misses using application, post-booking, and Fraud Operations data
Build monitoring and AI-powered workflows to detect model drift, population shifts, vendor degradation, data-quality issues, and new attack patterns
Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to product ionize changes
Validate impact and communicate recommendations to senior leadership and external partners
Help protect credit card programs while delivering fast, seamless member experiences

• 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field
• Strong Python and SQL skills
• 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 using fraud capture, loss rate, false-positive rate, approval impact, verification friction, operational workload, and economic value
• Ability to trace decisions through raw inputs, vendor responses, model scores, policy rules, and downstream outcomes
• Comfort owning projects end-to-end from problem definition through production implementation, monitoring, and business impact measurement
• Ability to communicate complex analytical findings and decision tradeoffs to technical and non-technical audiences
• Comfort using AI tools for analysis, investigation, feature development, documentation, and monitoring
• Experience with Snowflake, AWS infrastructure, dashboarding, and production monitoring tools

Demonstrates expertise in building and validating predictive models for fraud detection, identity verification, and KYC controls, while effectively communicating analytical findings to diverse audiences. Proficient in leveraging AI tools and statistical methods to enhance decision-making processes and operational efficiency.

• Python Programming
• SQL Proficiency
• Predictive Model Development
• Statistical Inference
• A/B Testing Design

• Data Science
• Risk Analytics
• Model Validation
• Feature Engineering
• Machine Learning
• Fraud Detection
• KYC Controls
• AML Compliance
• Data Transformation
• Decision System Evaluation

• Communication
• Project Ownership
• Analytical Thinking
• Collaboration
• Problem Solving

• Fraud Decisioning
• Identity Verification
• Application Fraud Models
• Credit Risk
• Operational Workload

• Snowflake
• AWS Infrastructure
• Dashboarding Tools
• Production Monitoring Tools
• AI Tools