Machine Learning Modeling Lead - Credit Modeling

EXL•San Francisco, CA

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 development and deployment of machine learning models that support business decisions in collections, credit risk scoring, fraud detection, customer segmentation, and personalized financial services. The candidate should bring strong knowledge of banking business, data, and customer-lifecycle domains, including bureau data, and provide technical leadership to junior modelers and data scientists.

Requirements

Strong business knowledge of banking analytics across the retail banking customer lifecycle.
At least 9 years of experience developing applied machine learning models in banking or financial services.
Hands-on experience leading machine learning projects and teams.
Strong experience developing, deploying, and monitoring models in production environments.
Familiarity with collections, underwriting, fraud, and ethical considerations in banking machine learning models.
Demonstrated 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, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and machine learning techniques including regression, decision trees, ensembles such as XGBoost, GBM, and random forests, and unsupervised learning.
Strong understanding 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 machine learning 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 enhancements.
Prepare and review documentation, model reviews, and submissions.
Manage queries raised by model validation teams.
Collaborate with implementation teams to deploy models in 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 new tools, technologies, and frameworks relevant to machine learning in finance.
Publish internal research and promote innovation and experimentation.