Data Scientist, Fraud Risk

Imprint

Data Scientist building fraud and identity decisioning for Imprint's modern credit-card programs. Developing models, experiments, and monitoring systems to reduce application abuse and friction.

Posted 9/4/2026full-timeCalifornia, New York • 🇺🇸 United StatesMid-LevelSenior💰 $170,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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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.

Highest-signal resume keywords

Python ProgrammingSQL ProficiencyPredictive Model DevelopmentStatistical InferenceA/B Testing Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceRisk AnalyticsModel ValidationFeature EngineeringMachine LearningFraud DetectionKYC ControlsAML ComplianceData TransformationDecision System Evaluation

Soft Skills

CommunicationProject OwnershipAnalytical ThinkingCollaborationProblem Solving

Tools & Technologies

SnowflakeAWS InfrastructureDashboarding ToolsProduction Monitoring ToolsAI Tools

Industry Keywords

Fraud DecisioningIdentity VerificationApplication Fraud ModelsCredit RiskOperational Workload

Tech Stack

Tools & technologies

AWSPythonSQL

About the role

Key responsibilities & impact

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 productionize changes
Validate impact and communicate recommendations to senior leadership and external partners
Help protect credit card programs while delivering fast, seamless member experiences

Requirements

What you’ll need

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

Benefits

Comp & perks

Competitive compensation and 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
Option to enroll in an FSA
20 weeks of paid parental leave for the primary caregiver
8 weeks of paid parental leave for all new parents
Access to industry-leading technology across all business units