Data Scientist - Credit - Remote

Overview

This position is central to enhancing the end-to-end model development process, from data exploration and credit risk modeling to model deployment and monitoring in production. This role bridges the gaps between data science and MLOps and is critical to ensure that models are analytically strong, operationally reliable, and compliant.

In Short

• Build and enhance credit risk models to improve credit predictability and manage portfolio risk.
• Work with MLOps and other teams to build adaptive modeling pipelines.
• Conduct exploratory data analysis and experiment tracking.
• Own Python project structure and CI/CD setup for reliable model delivery.
• Partner with teams to maintain model pipelines and streamline deployment.
• Support model deployment and validation for consistent results.
• Expand monitoring frameworks to underwriting models.
• Maintain audit-ready documentation for internal and external reviews.
• Support compliance reviews of model performance.
• Improve development efficiency through standardization and automation.

Requirements

• Bachelor’s degree in a quantitative field; Master's or PhD preferred.
• Solid understanding of the model lifecycle.
• Intermediate Python and SQL skills.
• Data wrangling using pandas.
• Basic version control experience.
• Experience with orchestration frameworks.
• Familiarity with cloud environments (AWS preferred).
• Model deployment experience.
• Unit testing and modern Python project tools experience.
• Background in credit risk modeling or related ML solutions.

Benefits

• Opportunity to work on critical projects in credit risk.
• Collaborate with cross-functional teams.
• Engage in innovative model development.
• Contribute to compliance and governance initiatives.
• Enhance skills in data science and MLOps.