Senior Product Manager

About this role

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Skills you'll use

What you'll do

• Copilot & Conversational Analytics
• Own the product definition for QAD's Copilot: how queries are understood, context assembled, data retrieved, and responses generated — specifying the contracts that engineering builds against
• Define the grounding contract: the rules that ensure every Copilot response cites governed manufacturing data, not hallucinated inference — including confidence signalling and graceful fallback behaviour
• Drive the conversational analytics strategy: how natural language queries translate into analytical results across manufacturing data domains without exposing SQL or BI complexity to the user
• Specify the disambiguation model: how the Copilot handles ambiguous queries, missing context, and conflicting data signals in real manufacturing workflows
• Own the product definition for QAD's semantic search layer: indexing strategy, query understanding, entity recognition, ranking, and result structure across manufacturing data domains
• Define the search intent taxonomy — operational (find order, trace shipment), analytical (show trends, compare periods), and diagnostic (why is this delayed, what caused this variance) — and specify result formats by intent type
• Drive the federated search architecture: how results from ERP operational data, analytical stores, and the knowledge graph are ranked and merged into a coherent, useful response
• Discovery, Delivery & GTM
• Maintain a structured customer discovery programme — extracting real manufacturing query patterns and translating findings into specific product and architecture implications

Who can apply

• Specify the disambiguation model: how the Copilot handles ambiguous queries, missing context, and conflicting data signals in real manufacturing workflows
• Own the product definition for QAD's semantic search layer: indexing strategy, query understanding, entity recognition, ranking, and result structure across manufacturing data domains
• Define the search intent taxonomy — operational (find order, trace shipment), analytical (show trends, compare periods), and diagnostic (why is this delayed, what caused this variance) — and specify result formats by intent type
• Drive the federated search architecture: how results from ERP operational data, analytical stores, and the knowledge graph are ranked and merged into a coherent, useful response
• Maintain a structured customer discovery programme — extracting real manufacturing query patterns and translating findings into specific product and architecture implications
• Own the product backlog: writing stories to Staff Engineer level of specificity — API contracts, state machines, data flows, edge cases — that engineering can build without verbal clarification
• Partner with GTM to define the launch and adoption strategy for Copilot and Search: positioning, sales enablement, onboarding playbooks, and the narrative that lands with manufacturing buyers
• Define the quality framework for these surfaces: what signals matter for product performance — retrieval relevance, grounding rate, latency distribution — and how they inform roadmap decisions

About QAD, Inc.

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