Senior Applied Scientist, Credit Risk

About the Role

As a key member of the applied science team, the senior applied scientist will own the full development lifecycle of machine learning models, from data exploration and feature development to model prototyping, deployment, monitoring, and iteration. This role requires strong technical depth, excellent communication skills, and the ability to thrive in a fast-paced, dynamic environment.

The senior applied scientist will report to the lead of the applied science team and collaborate with cross-functional stakeholders to drive business impact and inform product strategy.

What You Will Do

• Design, build, and optimize machine learning models to support credit risk decisioning and portfolio management
• Own the full applied science development lifecycle, from data exploration to model deployment and monitoring
• Investigate and evaluate new data sources, including structured and unstructured data, to integrate into credit models
• Develop backtesting, validation, and monitoring frameworks to evaluate model performance and business impact
• Apply machine learning, statistics, causal inference, optimization, and economics to solve core business problems
• Generate and communicate data-driven insights to influence product, risk, and company strategy
• Partner with product, business, engineering, and data stakeholders to translate ambiguous problems into clear objectives
• Contribute to best practices for model development, experimentation, documentation, testing, and production reliability
• Collaborate with data engineers to design and implement data pipelines and architectures
• Work closely with product managers to inform product roadmap and strategy

What We Are Looking For

• Bachelor's degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields
• 5+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent
• Strong familiarity with advanced statistics, machine learning, optimization, and/or economics
• Experience working with large datasets using Python and SQL
• Strong Python experience across exploratory data analysis, predictive modeling, and applied machine learning
• Strong communication skills, with the ability to bridge technical methodology to meaningful data narratives
• Track record of shipping high-quality machine learning products in production and at scale
• Ability to thrive in a fast-paced, dynamic environment

Nice to Have

• Experience with cloud-based data platforms and tools, such as AWS or GCP
• Familiarity with containers and orchestration tools, such as Docker or Kubernetes
• Knowledge of agile development methodologies and version control systems, such as Git

Benefits and Perks

• Competitive salary and equity package
• Comprehensive health, dental, and vision insurance
• Flexible PTO and remote work arrangements
• Professional development opportunities, including conference sponsorships and training programs
• Access to cutting-edge technologies and tools
• Collaborative, dynamic work environment with a team of experienced professionals

• Ensure you have a strong foundation in machine learning, statistics, and programming languages such as Python and SQL.
• Develop a portfolio of projects that demonstrate your ability to design, build, and optimize machine learning models.
• Practice communicating complex technical concepts to non-technical stakeholders, highlighting the business impact of your work.
• Be prepared to discuss your experience working with large datasets, data pipelines, and cloud-based platforms.
• Research Ramp's products and services, and be ready to discuss how your skills and experience align with the company's goals and mission.
• Prepare to backchannel references and be ready for a thorough interview process that may include technical assessments and case studies.