Position Overview
As the
Principal AI Strategy Leader, you will spearhead the strategic and methodological transformation of JD Power's Product, Technology, and Analytics (PT&A) organization. This high-impact leadership role is designed to transition traditional product development into an
Agentic Product Development Methodology.
You will bridge the gap between high-level business objectives-directly impacting EBITDA targets-and technical execution. You will act as the core advisor guiding both technical and non-technical Product Managers, engineering teams, and data scientists through the adoption, integration, and scaling of advanced generative AI and autonomous agentic workflows.
Key Responsibilities
- AI Strategy: Translate corporate growth goals into concrete initiatives, establishing clear business and technical KPIs to measure success across all portfolios.
- Methodological Framework Design: Blueprint and execute the transition from traditional product development to an advanced Agentic Product Development Methodology across 5 core teams.
- Baseline & Data Readiness: Architect and execute data readiness initiatives to define baselines, leveraging consumer insights, analytics, and benchmarking platforms.
- Tool Audit & Integration: Audit current early-adopter tool usage, manage enterprise AI agreements, and establish standard prototyping and usability testing platform integration.
- Infrastructure Optimization: Partner with engineering leaders to optimize cloud setups and manage dedicated compute/GPU resource allocations for localized model training.
- Enablement & Change Management: Drive tool adoption by upskilling non-technical PMs and engineers, turning abstract capabilities into practical, day-to-day development workflows.
PROFESSIONAL EXPERIENCE
- 10+ years of experience leading digital or product transformations, with at least 3+ years specifically focused on Enterprise AI strategy, LLM implementations, or Agentic workflow design.
- Technical Literacy: Strong conceptual and practical understanding of generative AI tech stacks, vector databases, model fine-tuning processes, and cloud infrastructure constraints (AWS/Azure/GCP and GPU resource management).
- Product Expertise: Proven track record in product management or technical architecture within data-dense, benchmarking, or consumer insights industries.
- Business Acumen: Demonstrated success aligning technical implementations with hard financial metrics (EBITDA, ROI, and cost-efficiency KPIs).
- Communication: Elite interpersonal and advisory skills; ability to easily communicate technical AI architectures to executive stakeholders and non-technical product teams alike.
