AVP Analytics Architecture- General Motors Insurance Company

GM Financial is building out the next great business within General Motors Insurance company that will disrupt the traditional model using their advantages as a subsidiary of the largest US automaker. The AVP of Analytics Architecture will own and evolve the end-to-end analytics data architecture and machine learning operations, ensuring effective scaling of analytics and machine learning capabilities across the entire business.

Responsibilities:

• Own analytics data architecture, including data transform, modeling, and serving layers
• Partner with Data Governance and IT Architecture to define and enforce data modeling standards (e.g., dimensional, semantic, or metric layers) to support self-service analytics and consistent metrics
• Lead architectural decisions around cloud data warehouses and ML orchestration frameworks
• Partner with analytics and business teams to ensure data platform is usable, trusted, and performant, not just technically elegant
• Establish technical best practices for data quality, lineage, metadata, and governance in collaboration with data governance team
• Design and operate the ML/AI platform supporting the full model lifecycle (experimentation, training, validation, deployment, and monitoring) in partnership with data science and engineering teams
• Determine the need and design of feature engineering stores to reduce friction from research to production
• Design and develop framework for model versioning & end-to-end reproducibility
• Build and operate a CI/CD for ML/AI solution that enables model deployment & monitoring into production systems at scale
• Collaborate with model governance, cyber security & architecture, privacy, cloud architecture and other stakeholders to maintain enterprise wide MLOps standards
• Set the technical vision and roadmap for analytics and ML platforms aligned to business strategy
• Make clear trade-offs between build vs. buy, speed vs. scale, and experimentation vs. operational rigor
• Lead architecture reviews and provide technical guidance on complex initiatives within the data and ML platforms
• Stay current on evolving data and ML platform technologies and assess relevance pragmatically
• Lead and mentor senior data engineers, analytics engineers, and ML platform engineers
• Establish clear technical standards, documentation, and operational practices for the data and ML platforms
• Collaborate with product, engineering, analytics, security, and infrastructure teams to ensure platform alignment and reliability
• Influence without authority across teams that depend on the data and ML platform