Senior Applied Scientist (Credit Risk)

Join Ramp, a fast-growing startup in the fintech industry, as a Senior Applied Scientist specializing in credit risk. In this role, you will design, build, and optimize machine learning models that support credit risk decision-making and portfolio management. You will work closely with business, product, data, and engineering partners to identify high-impact opportunities and translate ambiguous business problems into rigorous modeling work. This position offers a comprehensive benefits package, including medical, dental, and vision insurance, a 401(k) with employer match, unlimited PTO, and relocation support to NYC.

• 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.
• 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
• 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
• Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions
• 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
• PhD in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields
• Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)
• Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
• Experience leveraging AI/LLMs for development or for internal workflows
• Experience at a high-growth startup