Job Description: Applied Scientist, Credit

Salary: $183.6K - $252.5K Location Type: Hybrid Employment Type: Full-Time

About Ramp

Ramp is a financial operations platform designed to save companies time and money. Our all-in-one solution combines payments, corporate cards, vendor management, procurement, travel booking, and automated bookkeeping with built-in intelligence to maximize the impact of every dollar and hour spent.

More than 30,000 businesses, from family-owned farms to e-commerce giants to space startups, have saved $2B and 20M hours with Ramp. Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment platform in America, and enables over $55 billion in purchases each year.

Ramp’s investors include Thrive Capital, Sands Capital, General Catalyst, Founders Fund, Khosla Ventures, Sequoia Capital, Greylock, and Redpoint, as well as over 100 angel investors who were founders or executives of leading companies. The Ramp team comprises talented leaders from leading financial services and fintech companies—Stripe, Affirm, Goldman Sachs, American Express, Mastercard, Visa, Capital One—as well as technology companies such as Meta, Uber, Netflix, Twitter, Dropbox, and Instacart.

Ramp has been named to Fast Company’s Most Innovative Companies list and LinkedIn’s Top U.S. Startups for more than 3 years, as well as the Forbes Cloud 100, CNBC Disruptor 50, and TIME Magazine’s 100 Most Influential Companies.

About the Role

We’re looking for someone to help lead the future of credit applied science at Ramp. The Applied Science team at Ramp creates value by building the models powering decision-making. You will need to have a head for strategy & cross-functional collaboration, since you will partner closely with business & product stakeholders to prioritize, execute, and drive results through improving our Credit Risk decisioning systems. You will also partner closely with the rest of the data team and the engineering team to design, implement, and maintain data science models in production.

Applied scientists at Ramp focuses on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of causal inference, structural modeling, and optimization.

What You’ll Do

• Full stack data science development: from upstream data modeling and cleaning, to research and prototyping, to deploying and monitoring models in production and evaluating their business impact.
• Contribute to the company roadmap by working closely with stakeholders throughout the lifecycle of prioritization: from complex and nebulous business context, to well-defined objectives, to a roadmap of scoped opportunities for leveraging data science to drive business results.
• Improve model performance through new and improved data sources (e.g., accounting and bank statement parsing), advanced feature engineering, and model architecture enhancements.
• Ship production-grade ML pipelines including backtesting, retraining, drift monitoring, and business metric attribution.
• Design model evaluation and reporting frameworks that satisfy both internal stakeholders and external banking partners.
• Contribute to strategic decisions around risk policy and product expansion through ML-backed insights.
• Collaborate cross-functionally with engineering, product, and risk strategy teams to integrate models and optimize customer-level outcomes.

What You Need

• Bachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields.
• For candidates with Bachelor's and Master's, minimum of 5 years of industry experience as a Data Scientist, Applied Scientist, or equivalent.
• Experience working with large datasets in Python and SQL.
• Strong familiarity with the mathematical fundamentals of advanced statistics, optimization, and/or economics, as well as methods for experimental design and causal inference.
• Experience developing, deploying, and maintaining ML systems, including understanding of model performance monitoring, retraining, and feedback loop management in live environments.