Machine Learning Modeling Lead - Credit Modeling

EXL • San Francisco, CA

9d

About The Position

The Machine Learning Modeling Lead will join the ML Model Innovation team in the banking and fintech digital lending domain. The role leads the development and deployment of machine learning models that support business decisions across collections, credit risk scoring, fraud detection, customer segmentation, and personalized financial services. It requires strong banking knowledge, including data and domain expertise across the customer lifecycle and familiarity with bureau data.

The lead collaborates with cross-functional teams and provides technical guidance to junior ML modelers, data scientists, and engineers. The role oversees end-to-end model development and innovation, from data exploration and feature engineering through validation, deployment, and monitoring, and works with business stakeholders to turn banking challenges into data-driven solutions. It also supports model documentation and reviews, responds to validation team queries, and promotes research and experimentation.

Requirements

• 9+ years of experience in applied machine learning model development in banking or financial services.
• Strong business knowledge of banking analytics across the retail banking customer lifecycle.
• Hands-on experience leading machine learning projects and teams.
• Strong experience with model development, deployment, and monitoring in production environments.
• Familiarity with collections, underwriting, fraud, and ethical considerations in banking machine learning models.
• Demonstrable leadership, problem-solving, and people-management skills.
• Master’s degree or similar qualification in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.
• Expertise in Python, SQL, and machine learning libraries including NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
• Expertise in machine learning techniques including regression, decision trees, ensembles such as XGBoost, GBM, and Random Forest, and unsupervised learning.
• Strong grasp of statistical modeling, optimization, and deep learning techniques.
• Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.

Nice To Haves

• Knowledge of MLOps frameworks such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes.

Responsibilities

• Lead end-to-end ML solution development and innovation, including data exploration, feature engineering, model development, validation, deployment, and monitoring.
• Develop robust models that drive business benefits.
• Support and review junior scientists’ submissions and provide suggestions for improvement.
• Prepare and review documentation, conduct model reviews, and manage model submissions.
• Manage queries raised by model validation teams.
• Collaborate with implementation teams to deploy models into cloud or on-premises production environments.
• Work with business stakeholders to translate banking domain challenges into data-driven solutions.
• Guide junior data scientists and engineers on model development best practices.
• Evaluate tools, technologies, and frameworks relevant to machine learning in finance.
• Publish internal research and promote innovation and experimentation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior