Applied Scientist Internship, Ramp

• M.S. or Ph.D. Student: currently pursuing degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2026 - 2027
• Strong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization
• Python Proficiency: good grasp of common Data Science libraries (pandas, scikit-learn, NumPy, PyTorch, etc.)
• SQL Knowledge: experience wrangling data in a modern data warehouse (e.g. Snowflake, BigQuery, Redshift, Clickhouse)
• Practical Experience: track record of curating datasets and building/evaluating ML models
• Interest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutions
• Strong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrative
• Bias For Action: a comfort with ambiguity and desire to ship solutions quickly then iterate

Desirable

• The Applied Science team builds models and tools that solve Ramp’s most critical problems: from underwriting businesses to combatting fraud to making spend management smarter
• We’re deeply embedded in the business and provide a quantitative foundation for decision making
• As an Applied Science intern, you’ll be a fully integrated member of the team and own your project from start to finish
• Working with engineers, product managers, and business stakeholders, you’ll translate complex business needs into scalable machine-learning-driven solutions
• This is a chance to apply ML concretely, ship code, and create genuine value for Ramp and our customers
• You'll focus on exciting problems in areas like: credit, fraud, growth, or our core product!
• End-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoring
• State-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customers
• Versatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inference
• Rigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methods
• Collaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing features