Principal AI Leader

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.

• 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.

• 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.

• Engage in high‑impact AI modernization efforts across diverse industrial landscapes.
• Bridge functional gaps by partnering with strategy, product, data, and engineering stakeholders.
• Define the standards for ethical, responsible, and effective enterprise AI implementation.
• Acquire premier experience at the critical intersection of applied technology and business strategy.