Senior Product Manager, Data Intelligence & Knowledge Graph

<p><strong>About the position</strong></p> <p>Showpad is building a Revenue Intelligence engine that transforms raw signals—CRM data, email threads, transcripts, and content engagement—into prescriptive AI guidance. We are looking for a technical Product Manager to own the unified data intelligence layer that powers this engine.</p> <p>You will define how we capture and model data, and critically, determine the best retrieval and inference strategies (Search, RAG, Knowledge Graphs, or Vector Similarity) for every use case.</p> <p> </p> <p><strong>Key Responsibilities</strong></p> <ul> <li><strong>Data Modeling & Architecture</strong>: Define the canonical model for entities (deals, contacts, skills) and establish the retrieval strategy—choosing between keyword, dense vector, hybrid, or graph traversal based on latency and accuracy.</li> <li><strong>Search & RAG Ownership</strong>: Own the end-to-end RAG pipeline (chunking, embeddings, indexing). Write technical specs for retrieval layers in Meeting Prep and Roleplay AI, and define evaluation metrics (Recall@k, MRR).</li> <li><strong>Revenue Intelligence Roadmap</strong>: Drive the vision from "trusted foundation" to "prescriptive AI." Own the product logic for Deal Health and Winning Behavior models.</li> <li><strong>Platform Enablement</strong>: Act as the internal PM champion for the data platform, providing semantic primitives, event taxonomies, and data contracts to other product squads.</li> <li><strong>Discovery & Strategy</strong>: Conduct deep customer discovery and stay ahead of LLM/Search trends, translating fuzzy business questions into scoped ML problems.</li> </ul> <p> </p> <p><strong>Required Skills</strong></p> <p><em>Must haves</em></p> <ul> <li>5+ years in PM: at least 2 years specifically owning data platforms, search, analytics, or ML products.</li> <li><strong>Retrieval Expertise:</strong> Hands-on experience shipping RAG pipelines or search systems. You understand the trade-offs between BM25, vector search, and reranking.</li> <li><strong>Data Technicality</strong>: Fluency with Datalake architectures (Delta Lake, BigQuery, etc.) and strong entity-relationship modeling skills.</li> <li><strong>ML Fluency</strong>: Ability to define features, label designs, and evaluation metrics. You can review a technical design doc alongside engineers.</li> <li>Skilled at translating complex technical infrastructure into clear business value for executive stakeholders.</li> </ul> <p> </p> <p><em>Nice to haves</em>&