> Markdown version of [/jobs/ext/2732646-data-scientist-fraud-risk](https://www.wearedevelopers.com/jobs/ext/2732646-data-scientist-fraud-risk). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Fraud Risk - **Company:** S. Walker, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Data Transformation, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Backtesting, Rule Engine, Standard Sql, Feature Engineering, Model Validation, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/data-scientist-fraud-risk-imprint-2-9926864 ## About the Role * 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 Nice to Have * 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, Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let's move the world forward, together. ## Description The Risk team at Imprint builds the models, policies, and analytical systems that protect our credit card programs while delivering a fast and seamless member experience. As a Data Scientist focused on Onboarding Fraud, you will own the modeling and analytics that power fraud and identity decisions from application submission through account opening. Your goal will be to stop identity theft, synthetic identity, first-party fraud, and other forms of application abuse while minimizing false positives, unnecessary verification, and friction for legitimate applicants. You will partner closely with Fraud Strategy and Operations, Product, Engineering, Compliance, and Credit Strategy to improve onboarding fraud and KYC decisioning. You will build models, evaluate third-party fraud and identity vendors, test new scores and attributes, design experiments, and translate emerging fraud patterns into scalable policy changes. You will also build monitoring and AI-powered analytical workflows that detect shifts, diagnose root causes, and help the team respond quickly as fraud tactics evolve. The Opportunity * 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, Python and SQL for modeling and analysis. Snowflake for data warehousing. AWS infrastructure. Dashboarding and monitoring tools for production systems. ## Related Videos - [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c) - [WeAreDevelopers LIVE - GraalVM in action, Static Analysis insights and more](https://www.wearedevelopers.com/videos/1723-wearedevelopers-live-graalvm-in-action-static-analysis-insights-and-more) - [Hard Problems Hide in Boring Places: Turning Accounting Workflows into AI Products](https://www.wearedevelopers.com/videos/100226-hard-problems-hide-in-boring-places-turning-accounting-workflows-into-ai-products) - [Unleashing the power of AI to prevent financial crime](https://www.wearedevelopers.com/videos/1088-unleashing-the-power-of-ai-to-prevent-financial-crime) ## Related Articles - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies)