Senior Manager – Machine Learning

Role Overview

• Feature Engineering: Analyze internal and external datasets to surface trends and build high-signal features — including Debt-to-Income (DTI), Payment-to-Income (PTI), payment behavior patterns, and account balance signals — that improve the accuracy and predictive power of our credit models.
• Model Development: Design and develop strategic ML models end-to-end, including approval amount sensitivity models that optimize credit line assignment strategies and drive measurable business outcomes.
• Data Preparation: Manipulate, clean, and transform structured and unstructured data to ensure quality and readiness for modeling. Uphold rigorous standards for data integrity throughout the pipeline.
• Vendor Evaluation: Partner with external data vendors to assess third-party data products and scoring solutions. Lead cost-benefit analyses to guide partnership decisions and data integration strategies.
• Research & Innovation: Stay at the forefront of machine learning and feature engineering advances. Continuously incorporate new techniques to improve model performance and push the quality of our credit risk systems forward.
• Model Implementation & Validation: Collaborate with technology and engineering teams to implement and validate models with precision. Identify opportunities to improve the speed, accuracy, and repeatability of model deployment.
• Monitoring & Maintenance: Proactively track model performance in production. Lead calibration and redevelopment efforts to keep models accurate, reliable, and aligned with evolving business conditions.
• Compliance & Governance: Ensure all models meet regulatory standards and data vendor usage policies. Maintain thorough documentation of development processes, methodologies, and validation procedures. Support internal and external audit processes as needed.
• Cross-Functional Partnership: Work closely with risk management, engineering, finance, and sales to translate business challenges into scalable, data-driven solutions — and ensure seamless integration of models into the broader risk management framework.

Requirements

• Education: Master's or PhD in a quantitative discipline (Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field) preferred
• Experience: 5+ years of hands-on experience as a Data Scientist or Machine Learning Engineer, with a strong track record of building and deploying models in credit risk, fraud detection, or financial analytics
• Technical Skills:
• Advanced Python proficiency with demonstrated experience building production-grade ML models
• Strong SQL skills for data querying and manipulation
• Deep knowledge of ML algorithms including gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks
• Industry Background: Prior experience in lending, fintech, or financial services is highly preferred
• Governance: Familiarity with model risk governance frameworks and experience working with validation teams is a plus
• Communication: Able to translate complex technical concepts clearly for both technical peers and executive stakeholders.

Tech Stack

• Python
• SQL

Benefits

• Healthcare: We prioritize your well-being by covering 80% of medical, dental, and vision insurance costs, including coverage for your spouse, children, and other dependents.
• Retirement Benefits: Begin planning for your future from day one with our 401k plan.
• Paid Time Off: We understand the importance of work-life balance. That's why we offer flexible paid time off days starting from day one of your employment.