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# Machine Learning Modeling Lead - Credit Modeling

- **Company:** EXL SERVICE
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Analysis, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SQL Databases, Generic Buffer Management, Feature Engineering, Pytorch, Random Forest, Deep Learning, Pandas, Scikit Learn, Kubernetes, Information Technology, Xgboost, Machine Learning Operations, Docker, Unsupervised Learning
- **Published:** October 26, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5f923a7697848836

## About the Role

* Strong business knowledge of banking analytics across the retail banking customer lifecycle.
 * 9+ years of experience in applied machine learning model development in the banking or financial services domain.
 * Hands-on experience leading ML projects and teams.
 * Strong experience with model development, deployment and monitoring in production environments.
 * Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
 * Demonstrable leadership ability, superior problem solving and people management skills
 * Master's or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field

Skills :

 * Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
 * Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
 * Strong grasp of statistical modeling, optimization, and deep learning techniques.
 * Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.

## Description

* Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
 * Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
 * Responsible for documentation/documentation reviews, model reviews and submission
 * Responsible for managing queries raised by Validation teams for the model
 * Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
 * Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
 * Guide junior data scientists and engineers on best practices in model development
 * Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
 * Publish internal research and promote a culture of innovation and experimentation.