Senior Product Manager

Organization Overview

Role Overview

Copilot and Conversational Analytics

• Own Copilot product definition and specify contracts for query understanding, context assembly, retrieval, and response generation.
• Define grounding rules, confidence signalling, and fallback behavior for responses based on governed manufacturing data.
• Drive conversational analytics across manufacturing domains without exposing SQL or BI complexity.
• Specify disambiguation for ambiguous queries, missing context, and conflicting data signals.

Semantic Search

• Own indexing strategy, query understanding, entity recognition, ranking, and result structure across manufacturing data domains.
• Define operational, analytical, and diagnostic search intents and result formats.
• Drive federated search across ERP data, analytical stores, and the knowledge graph.

Discovery, Delivery and GTM

• Run structured customer discovery and translate manufacturing query patterns into product and architecture decisions.
• Own a detailed product backlog with API contracts, state machines, data flows, and edge cases.
• Define launch, positioning, sales enablement, onboarding, and adoption strategy with go-to-market teams.
• Define quality measures including retrieval relevance, grounding rate, and latency distribution.

Stakeholder Management

• Surface dependencies, risks, and scope changes with proposed resolutions.
• Communicate product and architecture decisions to non-technical stakeholders in business terms.
• Build credibility with Engineering, Architecture, and Go-to-Market through precise written work.

Product Experience

• 7–12 years of product management experience with significant ownership of enterprise B2B AI, search, or analytics products.
• Proven full-lifecycle ownership from discovery and specification through engineering delivery, launch, and go-to-market.
• Experience working across Platform, Engineering, Architecture, and Go-to-Market teams without direct authority.
• Structured discovery practice that produces product implications from research and usage data.

Required Technical Depth

• Semantic search architecture covering indexing, query understanding, entity recognition, ranking, and relevance evaluation.
• Conversational AI design for natural-language analytical experiences over complex domain-specific data.
• LLM product specification covering grounding, hallucination mitigation, quality gates, and context-window management.
• Data platform fluency across operational and analytical databases, federated search, and data freshness constraints.
• SQL and data-modelling knowledge sufficient to validate query plans and retrieval architecture trade-offs.

Additional Expectations

• Working familiarity with intent detection, context assembly, session and state management, and human-in-the-loop design is expected to grow into the role.
• Manufacturing ERP, operational analytics, or supply chain intelligence experience is preferred.
• The role is calibrated to a Staff Product Manager equivalent and has no initial people-management expectation.

About QAD