Machine Learning Modeling Lead – Credit Modeling

EXL

. Lead end-to-end ML solution development and innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring .

Posted 10/27/2026full-timeSan Francisco • California • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in end-to-end machine learning solution development, with a strong focus on banking analytics and model deployment in production environments. Proven ability to lead teams, mentor junior scientists, and translate complex banking challenges into actionable data-driven solutions.

Highest-signal resume keywords

Applied Machine Learning Model DevelopmentBanking Analytics ExpertiseModel Deployment and MonitoringPython and SQL ProficiencyMLOps Framework Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningFeature EngineeringStatistical ModelingDeep Learning TechniquesRegressionDecision TreesEnsemble MethodsUnsupervised LearningModel ValidationData Exploration

Soft Skills

Excellent Communication SkillsMentoring and Guidance

Tools & Technologies

NumpyPandasScikit-learnTensorFlowPyTorchMLflowKubeflowAirflowDockerKubernetes

Certifications & Qualifications

Master’s in Computer ScienceMaster’s in Data ScienceMaster’s in StatisticsMaster’s in Applied Mathematics

Industry Keywords

Banking DomainRetail Banking Customer LifecycleFraud ConsiderationsUnderwritingCollectionsEthical Considerations

Tech Stack

Tools & technologies

AirflowCloudDockerKubernetesNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Lead end-to-end ML solution development and innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring
Develop robust models that drive business benefits
Support and review junior scientist submissions and provide enhancement suggestions
Manage documentation, documentation reviews, model reviews, and submissions
Manage queries raised by Validation teams for the model
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 new tools, technologies, and frameworks relevant to ML in finance
Publish internal research and promote innovation and experimentation

Requirements

What you’ll need

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