Machine Learning Modeling Lead - Credit Modeling

Inizio Partners Corp; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Inizio Partners Corp. seeks a Machine Learning Modeling Lead for Credit Modeling in the Bay Area.

You will guide end-to-end ML solutions from data exploration to deployment, driving innovations in credit risk scoring, collections models, fraud detection, and customer segmentation. Ideal candidates have 9+ years in applied ML in banking/finTech, hands-on leadership, and strong production experience with Python, SQL, and modern ML frameworks.

Qualifications

• Advanced degree in a quantitative field preferred.
• 10+ years in ML/modeling with banking/fintech exposure.
• Proven leadership of ML projects and teams.
• Strong production deployment and monitoring experience.

Responsibilities

• Lead end-to-end ML solution development, including data exploration, feature engineering, modeling, validation, deployment, and monitoring.
• Develop robust models that drive business value; review junior submissions.
• Oversee documentation, model reviews, and regulatory/compliance reviews.
• Manage validations queries and support model governance processes.
• Collaborate with implementation teams to deploy models in production (cloud/on-prem).
• Translate banking domain challenges into data-driven solutions; mentor junior data scientists.

Skills

ML leadership

Credit modeling

Python

SQL

Feature engineering

Model deployment

Production monitoring

Stakeholder communication

Education

Master's in CS/DS/Math

Tools

MLflow

Kubeflow

Docker

Kubernetes

TensorFlow

PyTorch

Airflow

Job description

Machine Learning Modeling Lead - Credit Modeling

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 & innovationfrom 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.