Senior Product Manager

About the role

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
• Search
• 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
• 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

What you'll bring

• Product Experience
• 7–12 years in product management, with significant time owning AI-powered, search, or analytics products in enterprise B2B environments — from definition through production launch
• Proven track record owning the full product lifecycle: customer discovery, specification, engineering delivery, and GTM — not just one slice
• Experience working in matrixed organisations — driving outcomes across Platform, Engineering, Architecture, and GTM without direct authority
• Evidence of structured discovery practice applied to a technical product surface — ability to extract product implications from user research, not just UX insights
• Core Technical Depth — Required
• These are the primary technical domains this role owns. Strong command is expected from day one.
• Semantic search architecture: indexing strategy, query understanding, entity recognition, ranking models, and relevance evaluation at enterprise scale
• Conversational AI product design: how natural language queries are translated into structured analytical results across complex, domain-specific data without exposing backend complexity to users
• LLM-integrated product specification: grounding strategies, hallucination mitigation, response quality gates, and context window management — sufficient to define product contracts engineering builds against
• Data platform fluency: OLAP vs operational DB reads, federated search across heterogeneous stores, and how data freshness constraints affect product quality — sufficient to hold authoritative conversations with data engineers
• SQL and data modelling: sufficient to validate query plans, understand schema implications, and reason through retrieval architecture trade-offs