Lead Data Scientist

Overview

I'm currently working on a Lead Data Scientist opportunity with a growing fintech lender. They're looking for someone with deep expertise building and deploying credit risk underwriting models end-to-end, from business problem definition and stakeholder alignment through model development, deployment, monitoring, and ongoing optimization in production. The focus is specifically on underwriting and originations, including credit decisioning, approval/decline strategies, pricing, and terms optimization.

Responsibilities

• Lead the design, development, validation, and deployment of complex machine learning and statistical models in high-impact business areas.
• Drive end-to-end execution of key data science initiatives, from problem definition and model development to implementation, monitoring, and optimization.
• Establish the technical strategy and architecture for the company's modeling ecosystem, including underwriting, credit risk, pricing, and forecasting models.
• Partner closely with senior leaders across Risk, Product, Engineering, Operations, Sales, and Collections to identify opportunities where analytics can drive business outcomes.
• Define and uphold best practices for model governance, validation, code quality, documentation, and deployment standards.
• Serve as a technical leader and mentor to data scientists at all levels, helping elevate the team's capabilities.
• Communicate analytical insights, technical tradeoffs, and business recommendations to both technical and non-technical stakeholders, including executive leadership.
• Design and enhance production machine learning frameworks and monitoring systems to ensure scalable and reliable model performance.

Requirements

• Extensive experience developing and deploying credit risk and underwriting models within fintech, lending, banking, or other financial services environments.
• 10+ years of hands-on experience applying advanced statistical and machine learning techniques, including generalized linear models, gradient boosting, and deep learning.
• Demonstrated success leading high-impact data science initiatives from concept to production.
• Expert-level proficiency in Python, SQL, and Git.
• Experience deploying models within cloud-based environments such as AWS and making architectural decisions for scalable machine learning infrastructure.
• Strong understanding of statistical modeling, machine learning, and predictive analytics.
• Proven ability to influence technical direction, mentor senior team members, and drive organizational best practices.
• Excellent communication and stakeholder management skills, including presenting technical concepts to executive audiences.

Preferred Experience

• Experience building and supporting real-time decisioning systems and production-grade machine learning solutions.
• Cloud data platforms such as Snowflake and Databricks.
• Machine learning operations and monitoring tools.
• Workflow orchestration frameworks.
• Decision engines, feature stores, and modern model deployment architectures.
• Advanced degree (MS or PhD) in a quantitative discipline such as Statistics, Mathematics, Economics, Operations Research, Computer Science, or a related field.