VP, Modeling & Data Science

Set the enterprise modeling strategy across key domains: credit underwriting, fraud detection, marketing targeting, pricing, and operational decisioning. 

Champion AI/ML model innovation and oversee deployment of advanced statistical and ML models across the LendingClub ecosystem. 

Drive the development, enhancement, and governance of a comprehensive suite of models, ensuring performance, interpretability, and compliance. 

Collaborate with Technology to evolve our machine learning platform for scalable experimentation, deployment, and monitoring. 

Lead a team of 6–10 seasoned modeling and data science professionals, fostering a culture of innovation, curiosity, and rigor. 

Build robust partnerships with cross-functional teams including Credit Strategy, Marketing, Risk, Operations, Engineering, and Compliance. 

Evaluate and integrate emerging data sources to unlock new insights and opportunities across our lending and deposit products. 

Set the agenda for continuous improvement in tools, technologies, and methodologies. 

Serve as a modeling thought leader, representing LendingClub in industry forums and regulatory discussions, and benchmarking best-in-class practices. 

Partner closely with Model Risk Management to ensure strong governance and alignment with evolving regulatory expectations. 

Communicate complex technical and business topics with clarity and impact to senior leadership, the board, and regulators, on all aspects pertaining to the management of the modeling/AI/ML function. 

15+ years of relevant business experience, with a significant portion in consumer lending. 

10+ years of experience leading and developing teams of modelers, data scientists, or other analytical functions. 

Extensive hands-on experience with predictive modeling methods (e.g., logistic regression, multivariate linear regression, decision tree, cluster analysis), with a strong command of a wide range of advanced data mining and machine learning techniques. 

Deep practical experience and solid understanding of machine learning and deep learning methods (e.g., GBM, Neural Networks). 

Proficiency with leading machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). 

Experience establishing or scaling enterprise-level ML platforms and practices. 

Experience with consumer credit portfolios and data science/decision science/risk management within the banking sector is a significant plus. 

Hands-on knowledge of credit and fraud functions development in a regulated banking or fintech environment. 

Strong understanding of model governance, validation, and regulatory compliance in financial services. 

A systems thinker who is comfortable operating in both strategic and technical dimensions.  

Ability to develop sophisticated quantitative measurements and analyses to address multi-dimensional business needs. 

Exceptional communication skills, with the ability to clearly and precisely articulate technical and business topics across all levels of management, including senior executives and regulators. 

Proven ability to influence and drive change cross-functionally, championing new ideas and approaches. 

Degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics); a Master’s or PhD is preferred, though equivalent professional experience will also be considered.