Director, Modeling and Data Science

LendingClub•San Francisco, CA

1d•$210,000 - $245,000•Hybrid

About The Position

Happen Bank is seeking a Director, Modeling and Data Science to lead machine learning and statistical models supporting its lending and deposit products, including credit risk, fraud, collections, pricing, and marketing. The Director will lead experienced data scientists and set the direction for accurate, interpretable, and compliant modeling. The role partners with teams across Risk, Technology, Marketing, and Compliance to translate models into better business and member decisions. The position is based in San Francisco and follows a hybrid work model, with in-office attendance Tuesdays, Wednesdays, and Thursdays. Remote placement is not considered. The role primarily works local Pacific Time hours, with flexibility across time zones when needed, and includes travel to Happen Bank offices or other locations as needed. Relocation is offered based on actual job level. The target base salary range is $210,000–$245,000. The role may also be eligible for long-term equity awards and an annual bonus. Happen Bank offers a competitive benefits package.

Requirements

At least 10 years of experience in machine learning, data science, or credit risk analytics, preferably in consumer lending or financial services, including at least 6 years in people management; a bachelor’s degree in a related field or equivalent work experience.
Ability to lead a team with clear direction and accountability and make sound decisions about priorities, tradeoffs, and risk.
Strong technical depth in supervised and unsupervised learning, such as logistic regression, decision trees, gradient-boosting machines, and neural networks.
Proficiency in Python and key machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, and PyTorch.
Deep understanding of the model lifecycle, from development through monitoring and retraining.
Familiarity with model governance practices and regulatory frameworks, such as SR 11-7 and OCC guidance.
Understanding of how AI and machine learning models interact with credit risk decision-making, including their limitations, failure modes, and regulatory implications, and when a model should inform versus drive a decision.
Ability to identify where AI can add leverage and operationalize it within a team while maintaining accountability and fostering innovation.
Proven record of delivering complex cross-functional projects, managing stakeholder expectations, and bringing partners along as work evolves.

Nice To Haves

Hands-on experience developing LLMs and foundation models, such as transformer-based sequence models and pre-trained tabular models.
Master’s or PhD in a quantitative field such as Statistics, Computer Science, Engineering, or Economics.

Responsibilities

Lead the end-to-end lifecycle of machine learning and statistical models, from development and deployment through monitoring and retraining, across credit, fraud, collections, pricing, and marketing decisioning.
Set the modeling roadmap and functional priorities, balancing business impact, regulatory expectations, and team capacity.
Build, manage, and mentor a team of data scientists and machine learning experts, maintaining high standards for technical rigor and accountability.
Partner with Credit Strategy, Marketing, Risk, Engineering, Model Risk Management, and Compliance to align solutions with business goals and explain technical concepts to executives and oversight functions.
Apply advanced techniques such as gradient boosting, deep learning, and ensemble modeling to improve model performance.
Shape the bank’s machine learning infrastructure and tooling for scalable experimentation and deployment, and identify new data sources that can improve model performance.
Ensure rigorous model documentation, governance, and validation in line with financial services regulatory requirements, including model risk and fair lending expectations.
Embed AI into the team’s work to accelerate development, analysis, and documentation while maintaining human oversight, data integrity, and accountability.