Applied Scientist Intern
Skip the busywork
ApplyBolt rewrites your resume for this exact role and hits submit. You just pick the jobs.
About this role
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. 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!
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
- 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
Nice to Haves
- Publications, Projects, or Previous Experience: relevant experience applying AI/ML and demonstrating your passion for the field
- Production ML Mindset: knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code
- Data Orchestration: experience with leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow)