Director, Modeling and Data Science

Current Employees of Happen Bank: Please apply via your internal Workday Account

Happen Bank (formerly LendingClub) is built around a simple purpose: to clear the way to help people turn intention into action, and action into financial progress. That means offering focused products, a frictionless mobile-first experience, and clear terms with no gotchas. Respect and fairness is part of our DNA, and that ideal shapes how we work, how we treat each other, and how we invest in our employees and our community. Join us in using data, bold thinking, and a commitment to innovation to help clear the way for millions of Americans to achieve more.

About the Role

Happen Bank is looking for a Director, Modeling and Data Science to lead the machine learning and statistical models behind our lending and deposit products, including credit risk, fraud, collections, pricing, and marketing. You will lead a team of experienced data scientists and set the direction for how Happen Bank builds models that are accurate, interpretable, and compliant. You will work closely with partners across Risk, Technology, Marketing, and Compliance to turn those models into better decisions for the business and our members.

What You'll Do

• 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 priorities for the function, balancing business impact, regulatory expectations, and team capacity 

• Build, manage, and mentor a team of data scientists and machine learning experts, holding a high bar 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 complex 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 Happen Bank's ML infrastructure and tooling for scalable experimentation and deployment, and identify new data sources that 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 how the team works, using it to speed up development, analysis, and documentation while keeping human oversight, data integrity, and accountability in place 

About You

• 10+ years of experience in machine learning, data science, or credit risk analytics, preferably in consumer lending or financial services, including 6+ years in people management; bachelor's degree in a related field; or equivalent work experience 

• You lead the team with clear direction and sharp accountability, and you make sound calls on priorities, tradeoffs, and risk 

• You have strong technical depth in supervised and unsupervised learning (e.g., logistic regression, decision trees, GBMs, neural networks) and are proficient in Python and key ML libraries (e.g., Scikit-learn, XGBoost, TensorFlow, PyTorch) 

• You have a deep understanding of the model lifecycle, from development through monitoring and retraining 

• You are familiar with model governance practices and regulatory frameworks (e.g., SR 11-7, OCC guidance) 

• You understand how AI and ML models interact with credit risk decision-making, including their limitations, failure modes, and regulatory implications, and you know when a model should inform a decision versus drive it 

• You have a clear perspective on where AI adds real leverage across your function and have turned it into how your teams operate, fostering a culture of innovation while keeping accountability sharp 

• You have a proven record of delivering complex, cross-functional projects, managing stakeholder expectations, and bringing partners along as the work evolves 

Nice to Have 

• Hands-on experience developing LLM and foundation models, such as transformer-based sequence models, pre-trained tabular models  

• Master's or PhD in a quantitative field such as Statistics, Computer Science, Engineering, or Economics 

 

Work Location  

San Francisco 

 

The above locations are eligible offices for this role. The locations have been determined to foster in-person collaboration with this role’s team or the related business lines. We utilize a hybrid work model, and our teams are in-office Tuesdays, Wednesdays, and Thursdays. In-person attendance is essential for this role’s success, and remote placement will not be considered. Happen Bank offers relocation, based on actual job level.  

 

Time Zone Requirements  

Local hours (PT) 

 

While the position will primarily work local hours, Happen Bank is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary. 

 

Travel Requirements  

As needed travel to Happen Bank offices and/or other locations, as needed.  

 

Compensation  

The target base salary range for this position is 210,000-245,000. The base salary of the role will be determined by job-related knowledge, experience, education, skills, and location. Base salary is just one part of Happen Bank's Total Rewards package. You may also be eligible for long-term awards (equity) and an annual bonus (which is based on company performance, employee performance and eligible earnings). 

 

We’re creating new financial services solutions for our members based on fairness, simplicity, and heart, and we treat our employees the same way. We offer a competitive benefits package that includes medical, dental and vision plans for employees and their families, 401(k) match, health and wellness programs, flexible time off policies for salaried employees, up to 16 weeks paid parental leave and more.  

 

#LI-Hybrid 

#LI-BC1 

Happen Bank is an equal opportunity employer and dedicated to diversity, equity, and inclusion in the workplace. We do not discriminate on the basis of race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), gender, gender identity, gender expression, sexual orientation, age, marital status, veteran status, disability status, political views or activity, or other applicable legally protected characteristics. We believe that a variety of perspectives will make our teams and business stronger as we work together to transform the traditional banking system. 

 

We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at [email protected]. 

 

Notice on AI Tool Use 

For select roles and locations, candidate interviews may be recorded, transcribed and summarized by tools such as artificial intelligence (AI) to assist our hiring managers with the application process.

You will have the opportunity to opt out of recording, transcription, and summarization prior to any scheduled interviews. We will not discriminate against you if you choose to opt out.

During the interview, we will collect the following categories of personal information from or about you: contact information, identifiers, professional and employment-related information, sensory information (audio/video recording), and any other categories of personal information you choose to share with us.  We will use this information to evaluate your application for employment.

We will only share your interview, transcription, or summary with persons whose expertise or technology is necessary to process your application, evaluate your fitness for a position, and administer or support the tool. We will not sell your personal information or disclose it to any third party for their marketing purposes.  For more information about how we will handle your personal information, please refer to our Privacy Disclosure.

We will delete any recording of your interview promptly but in no event later than 30 days after making a hiring decision.