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AVP Analytics Architecture- General Motors Insurance Company
Why General Motors Insurance?
We are building out the next great business within General Motors Insurance company that will disrupt the traditional model using our advantages as a subsidiary of the largest US automaker. Our success depends on our ability to make disciplined, principled decisions at scale based on a foundation of rigorous data and machine learning. We will use data science and analytics to leverage our advantages in acquisition and telematics to create an insanely simple insurance product that GM vehicle owners love.
The AVP of Analytics Architecture is a senior technical leader who will own and evolve our end-to-end analytics data architecture and machine learning operations for General Motors Insurance Company. This role sits at the intersection of data engineering, analytics enablement, and production ML systems. You will be responsible for designing and scaling a modern data and ML platform that supports analytics, experimentation, and production-grade machine learning—while setting technical standards and best practices, mentoring data and analytics contributors, and partnering closely with analytics, data science, engineering, and product leaders. This is a hands-on leadership role: strategic in vision, pragmatic in execution, and deeply technical in architecture and platform design. Analytics and machine learning are crucial to our success at General Motors Insurance company; this role will ensure we can effectively scale these capabilities across the entire business.
About the role:
- 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.
What makes you an ideal candidate:
- Proven experience designing and operating modern analytics data platforms at scale.
- Hands-on experience with production ML systems and MLOps.
- Strong architectural judgment across data storage, compute, orchestration, and deployment patterns.
- Experience with the major cloud platforms, preferred experience with Azure
- Experience leading senior technical contributors and setting technical standards.
- Ability to translate business and analytical needs into durable technical solutions.
- Takes on ownership mentality and always pushes for continuous improvement.
- Inspires the team through strong leadership, coaching, and mentoring.
- Willing to go the extra mile as a manager with frequent check-ins, valuable feedback, and rigorous performance management.
- Experience supporting both BI/analytics workloads and near-real-time ML use cases.
- Familiarity with cloud-native architectures and infrastructure-as-code.
- Experience enabling self-service analytics and ML for non-platform teams.
- Background in regulated or data-sensitive environments.
Education
- Bachelor’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred
Work Experience
- 7-10 years of experience in data engineering, analytics platforms, ML infrastructure, or related roles required
5-7 years of experience leading technical teams in data engineering, ML engineering or related fields required
What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.
Our Culture: Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.
Compensation: Competitive pay and bonus eligibility
Work Life Balance : 100% remote
The base salary for this role is $140,000 to $246,000.
At GM Financial, we strive for transparency and in all aspects of our business, including pay equity. This is the GM Financial pay range for this role and job level. The exact salary and compensation will vary based on factors like knowledge, skills, experience and education.
This role is eligible to participate in a performance-based incentive plan. Full time employees are eligible to participate in health benefits on day one of employment.
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