Mission Lane is combining the power of data, technology, and exceptional service to pave a clear way forward for millions of people on the path to financial success. By attracting top talent and leveraging cutting-edge technology, we’re enabling people to unlock real financial progress.
Requirements
- Has experience applying data science to financial services
- Has created, deployed, and managed experiments and models in production systems.
- Practices solid fundamentals with software engineering (test-driven development, code review, refactoring) and the PyData stack (numpy, scikit-learn, pandas, etc.).
- Experience solving problems in consumer lending or fintech.
- Interest in advancing operations like fraud defenses, collections, and customer experience through the creation of new data sources, experimentation, and AI tools.
Responsibilities
- Lead the design, development, and deployment of machine learning models to solve practical problems and help our business and customers reach their financial goals.
- partner with business leaders and technical experts across the company to develop new data sources, improve our modeling methodology, and apply models with sound risk management.
- created, deployed, and managed experiments and models in production systems.
- Practices solid fundamentals with software engineering (test-driven development, code review, refactoring) and the PyData stack (numpy, scikit-learn, pandas, etc.).
- advancing operations like fraud defenses, collections, and customer experience through the creation of new data sources, experimentation, and AI tools.
Other
- Lead a data science team and cross-functional projects
- Can make your team better every day because you can be clear about goals and the team’s purpose. You can execute through education, organization, and trust.
- Can grow members of your team because you can help them identify their strengths and weaknesses, put them in a position to succeed, and get them the training and opportunities they need.
- Has a deep understanding of the field of data science and can communicate a vision to your team on how the field can be applied to solve difficult, practical problems.
- You can genuinely speak to practitioners about the fundamental principles and challenges required to be successful in applied work.
