Machine Learning Modeling Lead - Credit Modeling

Inizio Partners; San Francisco (CA); On-site; USD 170,000 - 230,000; Full time

Job summary

Inizio Partners seeks a Machine Learning Modeling Lead - Credit Modeling to steer the design, development, and deployment of advanced ML models for credit risk, collections, fraud, and customer segmentation within the banking/fintech space.

You will guide a cross-functional team of data scientists and engineers, translating banking challenges into data-driven solutions, and ensuring robust production-grade models with strong governance.

Qualifications

• 9+ years of experience in applied ML in banking/financial services.
• Hands-on experience leading ML projects and teams.
• Strong experience deploying ML models to production.
• Knowledge of collections, underwriting, fraud in banking ML models.

Responsibilities

• Lead end-to-end ML solution development and innovation from data exploration to deployment and monitoring.
• Develop robust models that drive business benefits and mentor junior scientists.
• Manage model reviews, documentation, and validation interactions.
• Collaborate with implementation teams to deploy models in production environments (cloud or on premises).

Skills

Python

SQL

ML libraries

Production deployment

Leadership

Education

Master's degree in CS/DS/Statistics or related field

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Job description

Location: Bay Area, CA

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.
• 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.