Intelligence Manager Jobs in Wisconsin (NOW HIRING)

Relativity is a leading legal data intelligence company building AI technology that helps organizations organize data, discover the truth, and act on it with confidence. Over two decades, the company has built the most trusted platform in legal data, earning deep relationships with the world's leading law firms, corporations, and government agencies, and managing petabytes of the most sensitive data in existence. That foundation is now being turned into something larger: The AI platform for legal data intelligence.

The Relativity Intelligence Model is the architecture for that transformation. At its base is the Foundational Layer (Relativity's shared data platform serving all AI applications). Five primitives give AI agents the structure, meaning, and retrieval capability they need to reason over legal data at scale: Files, Ontology, Data Capabilities, Knowledge/Metadata, and Query Plane. Relativity's Data Intelligence Foundation engineering org is building this layer. The product leadership that shapes it, drives adoption across Relativity's product teams, and builds the PM discipline to own it long-term. That's this role.

The Director of Product Management, Data Intelligence Foundation is one of the highest-leverage product roles at Relativity. The Foundational Layer is what makes every Relativity aiR application smarter, every agent more reliable, and every Relativity product team faster. Getting it right matters enormously.

Remote/Hybrid

• Files / Natives: The storage primitive for legal documents, images, and native files. You define the substrate that makes immutable legal data consistently accessible across every product, partner integration, and AI workflow, with the SLAs, access contracts, and API surface that teams can build on with confidence. The underlying data primitives are the foundation; the degree to which the retrieval layer (Query Plane) matches this structure determines how easily the organization can navigate between slow data and fast data use cases.
• Ontology / Relationship: The semantic layer of the Relativity Intelligence Model. Ontology encodes meaning: what kinds of things exist in legal data and how they relate, so that AI agents can reason, not just query. You define what Relativity's Ontology becomes: the entities, relationships, and contracts that give every Skill and Agent a shared vocabulary for legal data.
• Data Capabilities: Reporting, Audit, and internal data infrastructure. The operational backbone that makes the platform observable, auditable, and explainable. These are non-negotiable properties in legal data intelligence use cases.
• Knowledge / Metadata: The core data model that every product team, customer, and integration partner works with. A unified materialized document layer, consistent across all workspaces, is the mandate. Your roadmap evolves this surface to serve AI application teams as first-class consumers alongside the users who have relied on it for years.
• Query Plane: One of the most performance-sensitive and strategically important services in the product. The mandate is a unified retrieval pillar with a rich materialized document layer: standardized ingestion APIs independent of data source, hybrid retrieval (lexical + vector) with reranking, chunking as a managed capability, and tiered storage (cold/warm/hot).

• 12+ years in product management; 5+ years leading platform or infrastructure PM organizations
• Deep fluency with data platform primitives, including storage systems, metadata layers, knowledge graphs, query engines, or equivalent. You can design an API contract, contribute to engineering scope decisions, and articulate the trade-offs in a data consistency model.
• Demonstrated ability to manage and build a PM team through hiring, leveling, and establishing product practice for a new domain
• Bachelor's degree in Business, Computer Science, Engineering, or Design, or comparable work experience

• Experience building at companies where the data platform is the product, not a supporting system. Snowflake, Databricks, Elastic, MongoDB, Palantir, and similar are strong indicators of the right background.
• Experience building for AI systems, agents, or ML pipelines as primary consumers. You understand what a model needs from data that a human doesn't, and you design for both.

• Engineering credibility is paramount. You will be in technical discussions with skilled and knowledgeable engineering leaders regularly. You need to be a peer in those conversations, not a relay.
• Organizational cadence. You establish the operating rhythm for a multi-pillar PM org: the forum structure, planning cadence, and decision frameworks that let the team move with velocity and alignment. When priorities conflict across pillars, you hold the trade-off clearly and resolve it cleanly.
• Internal GTM ownership. Adoption of the Foundational Layer means every product team at Relativity builds with it. You define how internal teams discover, integrate, and get value from platform services, and you measure it. This is a product strategy job and a change management job simultaneously.
• Coaching a scaling PM org. You build PM capability, not just PM headcount, leveling people up while running at speed.
• Cross-functional influence without authority. You don't control the teams that need to adopt what you build. You make the new path clearly better and bring teams along through clarity, evidence, and trust.
• Directional clarity under ambiguity. Several of these primitives are being defined as the engineering teams build. You make good decisions with incomplete information and update them when required.
• Legal domain expertise is not required. However, internalizing why defensibility, chain of custody, and auditability are first-class design requirements.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$188,000 and $282,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.