[All remote jobs](https://jobicy.com/jobs.md)[![Imprint logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2024/02/a28c5741-221.jpeg)](https://jobicy.com/company/imprint.md)[Imprint](https://jobicy.com/company/imprint.md) # Data Scientist, Risk Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/imprint.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 170k–200k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished28 Sep 2026Apply before28 Oct 2026Listing views32Application actions0Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Imprint is seeking a senior-level Data Scientist to improve top-of-funnel credit decisioning for its co-branded credit-card programs. The role develops underwriting, targeting, and segmentation models, while optimizing approval rates without compromising credit quality. It requires rigorous experimentation, causal analysis, and channel-level economic modeling spanning losses, LTV, CAC, and contribution profit. The Data Scientist will also build AI-powered monitoring workflows to detect model or population shifts and recommend policy changes. Success depends on strong Python and SQL skills, credit-risk modeling experience, and clear cross-functional communication with Strategy, Product, Engineering, and Marketing. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a high-impact fintech risk role where modeling decisions directly affect approvals, expected losses, and acquisition profitability. It demands deep quantitative judgment, regulated-domain awareness, production-minded analytics, and end-to-end ownership in a fast-moving environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$185,000US market range$160k–$220k0$242k AI insightThe disclosed base salary range is USD 170,000–200,000 yearly, with a midpoint of USD 185,000. For a US-based senior Data Scientist specializing in consumer credit risk and underwriting, an estimated competitive market base-salary range is USD 160,000–220,000 yearly; equity and benefits may add meaningful total compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Credit Risk Modeling](https://jobicy.com/jobs?search_keywords=Credit%20Risk%20Modeling.md)[Underwriting](https://jobicy.com/jobs?search_keywords=Underwriting.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Statistical Inference](https://jobicy.com/jobs?search_keywords=Statistical%20Inference.md)[Causal Analysis](https://jobicy.com/jobs?search_keywords=Causal%20Analysis.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Segmentation](https://jobicy.com/jobs?search_keywords=Segmentation.md)[Financial Modeling](https://jobicy.com/jobs?search_keywords=Financial%20Modeling.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md) Sample interview questionsHow would you improve approval rates while preserving credit quality?I would first establish baseline approval, delinquency, loss, and profitability metrics by channel and applicant segment. I would identify policy constraints and model-score bands with favorable risk-adjusted economics, then run controlled champion/challenger tests with predefined loss and approval guardrails. I would scale only the changes that improve contribution profit while remaining within portfolio risk tolerances. How would you distinguish a policy change's impact from shifts in applicant mix? I would use randomized experiments where feasible, stratified by channel and relevant risk bands. When randomization is not possible, I would use causal methods such as difference-in-differences, matched cohorts, propensity weighting, and time controls. I would monitor mix variables, score distributions, and external factors to ensure observed outcomes are attributable to the policy rather than population changes. Describe how you would build a model for a new acquisition channel with limited performance history. I would begin with a transferable baseline using existing underwriting features and comparable-channel data, then calibrate it conservatively for the new channel. I would evaluate feature stability, expected selection bias, and early vintage outcomes, with stricter initial approval thresholds and exposure limits. As data accumulates, I would retrain or recalibrate the model and expand eligibility only when performance supports it. What would an effective AI-powered monitoring workflow for approval decisioning include? It would continuously track approval rates, score distributions, decline reasons, channel mix, model inputs, and early credit-quality indicators against expected ranges. The workflow should detect anomalies, identify likely drivers such as drift or policy changes, quantify potential business impact, and generate an auditable recommendation for review. Human approval and clear guardrails are essential before any production policy adjustment is implemented. How would you communicate a complex credit-risk recommendation to non-technical stakeholders? I would start with the decision required and the business trade-off, such as incremental approvals versus expected loss and contribution profit. I would use a concise narrative supported by a few clear visuals, segment-level results, assumptions, and confidence bounds. I would end with a concrete recommendation, rollout plan, risk guardrails, and the metrics that determine whether to continue, revise, or stop the change. Opportunity details ## About this role. ### Who We Are 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](http://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. ### Role Summary The Risk team at Imprint is responsible for making smarter, faster credit decisions that balance growth with responsible risk management. The team builds the models, policies, and analytical systems that power underwriting, fraud detection, and portfolio optimization across all of Imprint’s credit programs. As a Data Scientist, Risk, you will own the modeling powering Imprint’s top-of-funnel credit decisioning—from application intake through approval—across every acquisition channel: direct affiliates (Credit Karma, NerdWallet), invitation-to-apply emails, direct mail, paid social, instant prescreens, and on-site applications. Your primary focus will be improving approval rates while maintaining credit quality: building better underwriting models, designing policy experiments, and uncovering segments where we can safely expand access to credit. This role sits at the intersection of credit and acquisition strategy. You will partner directly with Credit Strategy, Product, Engineering, and Marketing to build targeting models for new channels, evaluate channel-level credit performance, and connect acquisition volume to downstream economics—approval rates, vintage loss forecasts, LTV, CAC, and contribution profit. Increasingly, that means building not just analyses but AI-powered systems that can autonomously monitor approval rate, channel performance, diagnose shifts, and recommend policy adjustments. ### The Opportunity * Own and improve the full top-of-funnel credit decisioning pipeline: application scoring, policy rules, decline waterfalls, and approval rate optimization across direct affiliates, invitation-to-apply, direct mail, paid social, instant prescreens, and on-site applications * Build and iterate on underwriting, targeting, and segmentation models that expand safe approvals and improve channel-level acquisition quality * Design and analyze A/B tests and champion/challenger experiments on credit policies, establishing a test-and-learn cadence with structured readouts on both acquisition and credit performance * Build channel-level performance models that connect application volume to downstream economics: approval rates, expected losses, LTV, CAC, and contribution profit * Design and build agentic workflows and AI-powered monitoring systems that autonomously detect approval rate anomalies, diagnose score drift and population mix changes, and recommend policy adjustments * Partner directly with Credit Strategy, Product, Engineering, and Marketing to develop targeting criteria and risk frameworks for new and emerging acquisition channels * Build segmentation frameworks to identify underserved populations where credit access can be responsibly expanded ### Your Profile 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 credit risk or targeting models (scorecards, underwriting models, segmentation) or similar predictive modeling in a regulated environment * Deep understanding of statistical inference, experimentation design, and causal analysis, with the ability to disentangle policy impact from population shifts and channel mix changes * Comfort with AI tools and AI-native workflows; you actively use tools like Claude, Copilot, or similar to accelerate your work and are excited to build AI-powered analytical systems * Full-stack problem-solving orientation: you dive into messy data, trace a decline to its root cause, and question assumptions in pursuit of a better answer * Ability to present complex findings clearly to technical and non-technical audiences, including senior leadership and external partner stakeholders * Comfort owning projects end-to-end in a fast-moving startup environment with limited scaffolding, collaborating cross-functionally with Policy, Strategy, Product, and Engineering Nice to Have * Experience with credit card underwriting, lending, or consumer credit products * Familiarity with credit bureau data (Vantage, FICO, tradeline attributes) and alternative data sources * Experience building or scaling experimentation infrastructure for credit policy testing * Exposure to fraud detection, KYC/IDV workflows, or application fraud models * Understanding of acquisition channel economics and experience partnering with marketing or credit strategy teams on targeting and LTV modeling 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 Learn more about how we build at Imprint on our engineering blog: [https://medium.com/imprint-eng](https://medium.com/imprint-eng) ### Perks & Benefits * 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. 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