Product Management Lead

Honeywell Technologies is a global automation company developing software, services, and solutions for industrial and process sectors. The Lead Product Manager will own offering management for Honeywell Forge cloud-hosted utility applications, defining product strategy and roadmaps while bringing AI capabilities to utility workflows. The role includes customer and market discovery, AI product strategy, and collaboration with engineering and R&D to deliver measurable value in utility operations.

Responsibilities:

• Product strategy and roadmap. Define product requirements, specifications, and roadmap for applications in the Forge Utility Software portfolio. Maintain alignment with the broader Forge platform architecture
• Analytics and Agentic AI roadmap. Own the product strategy for AI features across the portfolio, including AI for grid operations, DER management, rate design, and utility program performance. Translate AI capability into utility-relevant outcomes
• Customer and market discovery. Work with Solution Architects, Sales, and customers to identify the highest-value use cases. Conduct VOC and competitive analysis, especially against utility analytics and DERMS competitors
• Engineering partnership. Set product requirements and feed development priorities into the engineering / R&D organization

Requirements:

• 6+ years of software product management experience
• Cloud software product management experience. A track record of shipping cloud-native B2B software products. Comfortable with SaaS commercial models, cloud architecture trade-offs, and data platform considerations
• **Utility domain knowledge.**Familiarity with utility analytics, MDM, GIS/CIS integration, or DERMS use cases. Able to talk credibly with utility analysts and operations staff
• **Market-back product methodology.**Demonstrated history of building software product roadmaps from customer research, VOC, and competitive analysis rather than from internal ideation
• **AI / ML product fluency.**Working knowledge of how to scope, evaluate, and ship AI features in a B2B context. Understands the difference between an AI demo and an AI product, and can pressure-test feasibility, accuracy thresholds, and deployment risk with engineers
• Experience with utility data integration platforms (head-end systems, MDMS, data lakes, or similar)
• Experience defining commercial models for AI features (consumption-based pricing, tiered SaaS, etc.)
• Background working with utility regulators, EPRI, or industry consortia