Applied Scientist Intern

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 $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.

About the Role

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. You will focus on exciting problems in areas like credit, fraud, growth, or our core product.

What You'll Do

• 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.

What You Need

• Student Status: Currently pursuing a B.S., M.S. or Ph.D. in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2027 – 2029 (graduate degrees preferred, but not mandatory).
• Strong ML Fundamentals: Solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization.
• Strong Interest or Experience with AI: Curiosity and drive to integrate cutting-edge LLMs and agents into applied solutions.
• 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.
• 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: Comfort with ambiguity and desire to ship solutions quickly then iterate.

Nice to Haves

• Publications, projects, or previous relevant experience applying AI/ML.
• Production ML Mindset: Knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code.
• Data Orchestration: Experience leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow).