Want to know if this job is worth applying to?
Remote
Remote only
Imprint helps the world's best brands grow the lifetime value of their customers. We started with co-branded credit cards and rebuilt them to be smarter, more rewarding, and brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com, H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world-class operations allow us and our brand partners to offer powerful financial products without becoming a bank.
In the U.S., co-branded cards alone account for over $300 billion in annual spend, and most still run on decades-old legacy bank systems. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we're building a world-class team to redefine how people pay and how brands grow. If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.
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.
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
Required
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
We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply.
Stack
Python and SQL for modeling and analysis. Snowflake for data warehousing. AWS infrastructure. Dashboarding and monitoring tools for production systems.
Learn more about how we build at Imprint on our engineering blog: https://medium.com/imprint-eng
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
Additional health coverage includes 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
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.
