Associate Product Manager

Granicus is a technology company transforming the Govtech industry by connecting governments with their constituents. They are seeking an Associate Product Manager to develop and scale AI products that enhance their Revenue Operations, focusing on creating practical, data-driven solutions for sales and operational needs.

Responsibilities:

• Define and execute actionable plans to improve the health, usability, and effectiveness of internal AI products supporting Revenue Operations, including sales execution, forecasting, deal governance, proposals, and enablement workflows
• Own prioritization of AI capabilities, enhancements, and experiments—setting clear scope, sequencing, and delivery timelines aligned to RevOps objectives and capacity
• Leverage data‑driven insight to determine what to build, when to deliver, and how to right‑size AI solutions, balancing business value, data readiness, and technical complexity
• Communicate priorities, tradeoffs, and progress clearly to RevOps and senior leadership, ensuring alignment on outcomes, risks, and expected value
• Apply a strong product and user‑experience mindset to internal AI tools, ensuring solutions drive high adoption, trust, quality, and productivity across revenue teams
• Develop deep subject‑matter expertise in targeted RevOps domains (e.g., forecasting, deal inspection, RFPs, proposals, sales workflows) through stakeholder discovery, workflow analysis, usage data, and performance outcomes
• Build alignment and momentum with engineers, data teams, product leaders, and RevOps stakeholders to move prioritized AI initiatives from concept through production and sustained use
• Define, track, and report the metrics that determine success of AI initiatives, including adoption, accuracy, time saved, process efficiency, deal quality, and revenue impact
• Monitor post‑release performance of AI services and enhancements, assessing usage, effectiveness, and downstream business impact, and iterate based on findings
• Act as the internal voice of RevOps users by continuously incorporating feedback from Sales, Enablement, Proposal, and Operations teams into roadmap decisions and solution improvements

Requirements:

• A BA/BS in Business, Computer Science, Engineering, Data Science, or a related field—or equivalent experience delivering AI‑enabled or data‑driven products in an operational environment
• A passion for building high‑quality internal AI products and platforms in fast‑moving, ambiguous environments where priorities, inputs, and constraints evolve rapidly
• Hands‑on experience working with or alongside AI/ML systems, such as predictive models, recommendation engines, NLP/LLM‑based solutions, workflow automation, or AI agents—focused on applied business impact rather than pure research
• Familiarity with modern AI product concepts, including model limitations, data quality and labeling, prompt design, evaluation metrics, human‑in‑the‑loop workflows, and responsible AI considerations
• Working knowledge of agile product development practices (Scrum, Lean), with an emphasis on iterative delivery, experimentation, and continuous learning—especially in AI‑driven environments
• Strong intellectual curiosity that drives you to ask sharp questions, pressure‑test assumptions, and use data to identify and prioritize the highest‑impact RevOps opportunities for AI
• Excellent organizational and quantitative analytical skills, with the ability to translate complex workflows, data signals, and AI outputs into clear product direction and actionable plans
• Sufficient technical depth to collaborate effectively with engineers and data teams, understand tradeoffs between model performance, cost, latency, and scalability, and earn trust through informed decision‑making
• The ability to break down complex AI initiatives into manageable, well‑sequenced increments that deliver user and business value quickly and safely
• Strong communication skills, with the ability to tailor messages for technical teams, RevOps stakeholders, and senior leaders—clearly explaining value, risk, limitations, and expected outcomes of AI solutions