The RoleWe're seeking a data science leader and practitioner to dream, design, and build machine learning foundations to drive our exciting growth agenda. The
VP/MD of Data Science will focus on building the foundational data science capabilities to enable the integration and growth of our emerging
Auto Finance businesses. This role will partner closely with credit, operations, and business leaders of our legacy direct auto and recently acquired indirect auto programs. This leader will set the data science strategy, build a team, and create core data science use cases and capabilities to enable rapid growth.
Reporting to the Head of Data Science, this is a critical leadership position that requires key characteristics including the ability to partner and influence across stakeholders, apply strategic thinking, practical business problem solving, a forward leaning results focus, and team leadership and development. This is a rare and exciting opportunity to own the design and development of advanced machine learning capabilities spanning underwriting, marketing, operations, and risk, in a company which combines the best of FinTech and Banking.
This individual will be charged with both building the future while delivering impact in the present and must have both the skills and orientation to get "hands on" the keyboard, particularly in the early days. This leader must also have a strong model risk management orientation - skilled in using "black box" approaches, while remaining skeptical and continuously challenging the team.
Responsibilities- Collaborate, influence, and "bring along" business and technology leaders to maximize the impact of machine learning solutions in the auto finance businesses
- Design and build the foundational data science practices and develop the decision making algorithms (using traditional regression models and machine learning algorithms) to enable rapid and sustained growth of auto finance lending
- Research and develop new modeling techniques that will keep OneMain at the forefront of the industry
- Research and develop with both traditional and non-traditional data sources (such as information from other industries, tax data, deposit data, demographic data, digital data) to find insights which can be used in models to keep at the forefront of the industry
- Apply diverse statistical and machine learning techniques to analyze a variety of datasets to solve complex, unstructured business problems
- Work with wide range of open-source tools to analyze and extract value from granular tradeline level granular data
- Recruit, develop, and lead a high performing and high potential team of data scientists to conduct statistical analysis and develop profit-driven decision framework and risk strategies
- Independently complete analyses, draw conclusions, and present results to all levels of leaders in order to influence business/strategy decision
- Partner with technology leaders to build and evolve an advanced machine learning environment for auto finance
- Effectively prioritize competing initiatives across direct reports; translate strategic priorities into individual/team initiatives and manage expectations
- Collaborate with Model Risk, Legal, Compliance, Audit, and other risk management functions to ensure solutions are developed that meet expected standards and policies
Qualifications- Master's Degree in a quantitative discipline (Engineering, Statistics, Economics, Biostatistics, Physics, Computer Science, or related field); PhD preferred
- 6+ years of progressively broader roles in model development and implementation experience; at least 4 of those years in consumer lending (credit card; unsecured loans; auto lending; or home equity); auto finance experience is a plus, but an enthusiasm and ability to rapidly learn a consumer lending asset class is more important
- Expert experience designing, developing, and implementing machine learning solutions - must have experience developing and maintaining credit underwriting machine learning systems in a FCRA governed context
- Applied experience in a variety of modeling techniques like bagging, boosting, NLP and so forth, as was feature engineering
- Proven experience in communicating with stakeholders across all phases of machine learning modeling project, including validation, deployment, and ongoing monitoring
- Proven experience in assessing, mentoring, and developing junior data science talent
- Advanced experience in use of open-source machine learning libraries (Python or R) in a cloud-based environment
- Experience in identifying and managing the risks of machine learning systems
- Innovative and capable of developing creative solutions to complex, data-driven problems
