Machine Learning Modeling Lead - Credit Modeling
At a glance
Senior Machine Learning Engineering role at Fa Ewjt Saasfaprod1. San Francisco · full-time · no salary stated · we estimate $208,750–$296,750.
Fa Ewjt Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead model work from data exploration and feature design through development, validation, production release and monitoring
- Build models for credit risk, collections, fraud, customer grouping and personalized financial services
- Review junior scientists’ work and model documentation, suggest improvements, and handle questions from validation teams
- Work with implementation teams to put models into cloud or on-premises production environments
- Translate banking challenges with business stakeholders into data solutions, while guiding junior technical staff
What you bring
- At least nine years developing applied machine learning models in banking or financial services
- A master’s degree or equivalent in a quantitative discipline such as computer science, statistics or data science
- Hands-on leadership of machine learning projects and teams, with production model development, deployment and monitoring experience
- Strong retail banking lifecycle knowledge, including collections, underwriting, fraud and the ethical use of banking models
- Expert skill in Python, SQL and machine learning methods, including regression, tree-based approaches and deep learning
Who this fits
This senior role suits someone who combines banking analytics expertise with hands-on model leadership and can explain technical work to nontechnical stakeholders. The position is hybrid in San Francisco and includes guidance for junior data scientists and engineers.
From the employer
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.
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