About the role
About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $100B in annualized spend flows in and out of 50,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About
the Role We’re looking for a Senior Applied Scientist to help drive the future of credit applied science at Ramp. In this role, you will design, build, and optimize the models that power our credit risk systems, helping us make faster, smarter, and more scalable risk decisions for our customers. You’ll work at the intersection of machine learning, statistics, economics, and product strategy. This role requires strong technical depth as well as close collaboration with business, product, data, and engineering partners. You will help identify high-impact opportunities, translate ambiguous business problems into rigorous modeling work, and ship models that operate reliably in production. Applied scientists at Ramp focus on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of machine learning, causal inference, structural modeling, and optimization.
What You'll Do Design, build, and optimize machine learning models that support credit risk decisioning and portfolio management at Ramp Own the full applied science development lifecycle, from data exploration and feature development to model prototyping, deployment, monitoring, and iteration Investigate and evaluate new data sources, including structured and unstructured data, and integrate them into credit models where appropriate Develop backtesting, validation, and monitoring frameworks to evaluate model performance and business impact Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems Generate and communicate data-driven insights that influence product, risk, and company strategy Partner with product, business, engineering, and data stakeholders to translate ambiguous problems into clear objectives, scoped opportunities, and a practical applied science roadmap Contribute to best practices for model development, experimentation, documentation, testing, and production reliability What You Need Bachelor’s degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields. 5+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 3+ years of industry experience with a PhD Strong familiarity with the mathematical fundamentals of advanced statistics, machine learning, optimization, and/or economics Experience working with large datasets using Python and SQL Strong Python experience across exploratory data analysis, predictive modeling, and applied machine learning, using tools such as NumPy, pandas, scikit-learn, PyTorch, or similar libraries Strong communication: the ability to bridge technical methodology to meaningful data narratives to drive company decisions and strategy Track record of shipping high-quality machine learning products in production and at scale Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions Nice-to-Haves PhD in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development) Familiarity with data orchestration platforms (Airflow, Dagster, Prefect) Experience at a high-growth startup Experience leveraging AI/LLMs for development or for internal workflows