Senior Applied Scientist, Credit Risk

Ramp; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Ramp is seeking a Senior Applied Scientist to develop and optimize credit risk models using machine learning and statistics. This role requires strong Python and SQL skills and a background in quantitative fields.

As a Senior Applied Scientist at Ramp, you will design models across the lifecycle—from data exploration to deployment and monitoring—ensuring robustness and business alignment. You will work with product, engineering, and data teams to translate complex problems into actionable

Qualifications

• Strong Python and SQL skills.
• Background in quantitative fields.
• Experience deploying ML models into production.
• Knowledge of credit risk concepts and portfolio management.
• Ability to translate business problems into data-driven solutions.

Responsibilities

• Design and optimize ML models for credit risk decisions.
• Collaborate with product, eng, and data teams.
• Monitor model performance and ensure robustness.
• Translate insights into actionable business recommendations.

Skills

Python

SQL

Machine Learning

Statistics

Model Deployment

Causal Inference

Education

Quantitative degree

Job description

Ramp is seeking a Senior Applied Scientist to develop and optimize credit risk models using machine learning and statistics. This role requires strong Python and SQL skills and a background in quantitative fields. Ideal candidates are experienced in model deployment and thrive in fast-paced environments.

As a Senior Applied Scientist at Ramp, you will design and optimize machine learning models that enhance credit risk decision-making and portfolio management. This role encompasses the entire applied science lifecycle, from data exploration to model deployment and monitoring, ensuring models are robust and aligned with business objectives.

You will collaborate with product, business, engineering, and data teams to translate complex problems into actionable insights. Your work will directly impact how Ramp's customers manage their financial decisions, requiring a strong foundation in statistics, optimization, and causal inference to solve critical business challenges.