Data Scientist, Risk

Imprint; EE. UU.; A distancia; USD 130.000 - 200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Equity packages

Flexible paid time off

Fully covered healthcare

One Medical access

Parental leave

Descripción de la vacante

Imprint is seeking a senior Data Scientist, Risk, to optimize the top-of-funnel credit decisioning across multiple channels. You will build and improve underwriting models, design experiments, and partner with policy, product, and engineering to drive growth with controlled risk.

You will leverage Python/SQL and AI tools to monitor performance, diagnose drift, and deliver actionable insights to senior leadership, shaping how brands access credit at scale.

Formación

• 5 to 8+ years of data science, risk analytics, or related quantitative field.
• Experience building credit risk or targeting models (scorecards, underwriting models, segmentation).
• Strong Python and SQL; ability to transform raw data and build datasets.
• Understanding of experimentation design and causal analysis.
• Comfort with AI tools (Claude, Copilot) to accelerate work.
• Ability to present complex findings to technical and non-technical stakeholders.

Responsabilidades

• Own the full top-of-funnel credit decisioning pipeline across multiple channels.
• Build and iterate underwriting, targeting, and segmentation models to expand safe approvals.
• Design and analyze A/B tests and experiments on credit policies.
• Connect application volume to downstream economics (loss forecasts, LTV, CAC).
• Develop AI-powered monitoring systems to detect rate anomalies and policy shifts.
• Collaborate with Credit Strategy, Product, Engineering, and Marketing.

Conocimientos

Python

SQL

Credit risk models

Experimentation

AI tooling

Data storytelling

Cross-functional collaboration

Herramientas

Snowflake

AWS

Descripción del empleo

Who We Arethe company 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. the company 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 SummaryThe Risk team at the company 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 the company’s credit programs.

As a Data Scientist, Risk, you will own the modeling powering the company’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, ideal 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

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

the company is committed to a diverse and inclusive workplace. the company 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. the company 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.

StackPython and SQL for modeling and analysis. Snowflake for data warehousing. AWS infrastructure. Dashboarding and monitoring tools for production systems.

Learn MoreLearn more about how we build at the company on our engineering blog: https://medium.com/the company-eng