Machine Learning Modeling Lead - Credit Modeling

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

We are seeking a highly skilled and experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team within the banking/fintech domain (digital lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services.

The Individual needs to have strong knowledge of banking business, data and domain across the customer lifecycle as well as bureau data. They will collaborate with cross-functional teams and provide technical leadership to junior ML modelers and data scientists.

Key Roles and Responsibilities -

• 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.

Candidate Profile:

• Strong business knowledge of banking analytics across the retail banking customer lifecycle.
• 12+ 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.

What we offer:

• EXL Analytics offers an exciting, fast paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.
• You will learn effective teamwork and time-management skills - key aspects for personal and professional growth
• Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.
• We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors.
• Sky is the limit for our team members. The unique experiences gathered at EXL Analytics sets the stage for further growth and development in our company and beyond.

The typical base pay range for this role across the U.S. is USD $202,000 - $280,000 per year.

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale.

Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.

The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.