Director, Modeling and Data Science

LendingClub•San Francisco, CA

2d•Hybrid

About The Position

Happen Bank is seeking a Director, Modeling and Data Science to lead the machine learning and statistical models supporting its lending and deposit products, including credit risk, fraud, collections, pricing, and marketing. The Director will lead an experienced data science team and define how the bank builds accurate, interpretable, and compliant models. The role partners with teams across Risk, Technology, Marketing, Engineering, and Compliance to translate modeling into better business and member decisions. The position is based in San Francisco and follows a hybrid work model, with in-office attendance on Tuesdays, Wednesdays, and Thursdays. In-person attendance is essential, and remote placement will not be considered. The role primarily works local Pacific Time hours, with flexibility across time zones as needed, and requires travel to bank offices or other locations as needed. Relocation may be 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. Benefits include medical, dental, and vision plans for employees and their families, a 401(k) match, health and wellness programs, flexible time-off policies for salaried employees, and up to 16 weeks of paid parental leave.

Requirements

10 or more years of experience in machine learning, data science, or credit risk analytics, preferably in consumer lending or financial services, including 6 or more years in people management; a bachelor's degree in a related field or equivalent work experience.
Ability to provide clear direction and accountability and make sound decisions about priorities, tradeoffs, and risk.
Strong technical depth in supervised and unsupervised learning, including methods such as logistic regression, decision trees, gradient-boosted models, and neural networks.
Proficiency in Python and key machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or 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 model limitations, failure modes, and regulatory implications, and the ability to judge when a model should inform versus drive a decision.
A clear perspective on where AI adds leverage and experience applying it to team operations while maintaining accountability.
A 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 large language models and foundation models, such as transformer-based sequence models or pre-trained tabular models.
A master's degree or Ph.D. 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—for credit, fraud, collections, pricing, and marketing decisioning.
Set the modeling roadmap and 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 clearly 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 data sources that can improve model performance.
Ensure rigorous documentation, governance, and validation in line with financial-services regulatory requirements, including model-risk and fair-lending expectations.
Embed AI into team workflows to accelerate development, analysis, and documentation while maintaining human oversight, data integrity, and accountability.