Senior ML Modeling Lead for Credit & Risk Innovation

EXL; San Francisco (CA); On-site; USD 202,000 - 280,000; Full time

Benefits offered by this job

Mentoring program

Job summary

EXL seeks a highly experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team in the banking/fintech space (digital lending). This leader will drive end-to-end ML solution development, from data exploration and feature engineering to validation, deployment, and monitoring, guiding junior scientists along the way.

The ideal candidate has 12+ years in applied ML within banking or financial services, with hands-on leadership and experience deploying models

Qualifications

• 12+ years of experience in applied machine learning model development in banking/financial services.
• Hands-on leadership of ML projects and teams with production deployment experience.
• Strong banking analytics knowledge across retail banking lifecycle and data domains.

Responsibilities

• Lead end-to-end ML solution development from data exploration to deployment and monitoring.
• Develop robust models that drive business benefits and review junior scientist submissions.
• Manage model documentation, validation responses, and inquiries from validation teams.
• Collaborate with implementation teams to deploy models in production environments (cloud or on‑premises).
• Translate banking domain challenges into data-driven solutions with stakeholders.
• Guide junior data scientists and engineers on best practices and evaluate new ML tools.

Skills

Python

SQL

Numpy

Pandas

Scikit-learn

TensorFlow

PyTorch

MLops

Education

Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or related quantitative field

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Job description

EXL seeks a highly experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team in the banking/fintech space (digital lending). This leader will drive end-to-end ML solution development, from data exploration and feature engineering to validation, deployment, and monitoring, guiding junior scientists along the way.

The ideal candidate has 12+ years in applied ML within banking or financial services, with hands-on leadership and experience deploying models