Senior Applied Scientist, Credit Risk

Responsibilities

• Design, build, and optimize machine learning models that support credit risk decisioning and portfolio management
• Own the full applied science development lifecycle from data exploration to deployment and monitoring
• Investigate and integrate new structured and unstructured data sources into credit models
• Develop backtesting, validation, and monitoring frameworks to evaluate model performance
• Apply machine learning, statistics, causal inference, and optimization to solve core business problems
• Partner with product, business, engineering, and data stakeholders to translate ambiguous problems into technical roadmaps

Requirements

• Bachelor’s degree or above in a quantitative field (Math, Economics, Statistics, CS, etc.)
• 5+ years of industry experience as an Applied Scientist or ML Engineer (or 3+ years with a PhD)
• Strong mathematical fundamentals in statistics, machine learning, and optimization
• Proficiency with large datasets using Python and SQL
• Extensive Python experience with libraries such as NumPy, pandas, scikit-learn, or PyTorch
• Proven track record of shipping high-quality machine learning products in production at scale
• Strong ability to communicate technical methodology to drive business strategy

Preferred Qualifications

• PhD in a quantitative field
• Strong understanding of the data science engineering development cycle
• Familiarity with data orchestration platforms like Airflow, Dagster, or Prefect
• Experience at a high-growth startup
• Experience leveraging AI/LLMs for development or internal workflows

About the Company

Ramp is building the smart infrastructure for finance teams, automating how over $200B in annualized spend flows in and out of 70,000+ companies through payment authorization, risk flagging, and spend categorization.