Director II, Enterprise Data Science and AI Fundamentals
State Auto
- Boston, MA Seattle, WA
- Permanent
- Full-time
- Broadly set vision and act as technical/scientific advisor for how to improve our Enterprise DS practice, via a combination of development programs, risk management processes, community culture, streamlining best practices, and similar initiatives.
- In partnership with Enterprise Risk Management, Global Compliance, and key DS leaders, create and streamline a Model Risk Management program to identify our current AI footprint.
- Help align research and governance initiatives for Generative AI and other next-generation tech, and create programs to ensure we experiment with integrity and speed.
- Help define and measure technical capabilities such as Machine Learning Operations (MLOps), High Performance Engineering, etc., and create programs and practices to drive adoption of best practices.
- Help collect, curate, and refine best scientific practices across the enterprise, establishing common documentation, knowledge sharing programs, training curricula, and development programs.
- Create and maintain a culture of continuous learning and collaboration across DS teams.
- In partnership with our Data Offices and Legal counsel, help drive strategy for advanced privacy protection using novel technologies, including Privacy Preserving Analytics, Synthetic Data, Federated Learning, Multiparty Communication, and other techniques to help us better protect our customers’ data while driving powerful insights.
- Work with business units across Liberty to educate DS, executives, and business users on ethical considerations around AI, including proper usage of AI, algorithmic bias, and risk management.
- Broad experience with hands-on modelling and DS lifecycle activities, particularly in Python, and ability to communicate at a high level with DS and non-technical leaders
- Demonstrated ability to build trust with stakeholders
- Demonstrated experience leading DS projects
- Proven ability to lead and drive high profile cross-functional projects and teams
- Broad understanding of emerging scientific trends and techniques in MLOps, privacy, interpretability, explainability, risk management, bias and fairness, hallucinations, etc.
- Competencies typically acquired through an advanced degree (in Statistics, Mathematics, Data Science or other relevant field of study) and 8 years of relevant experience or may be acquired through a Bachelor’s degree (scientific field of study) and 10 years of relevant experience.