Director, Product Management - Data Management & Data Products

Overview

At Lexis Nexis Intellectual Property Solutions (LNIP), our mission is to bring clarity to innovation by enabling innovators to make informed decisions, accelerate productivity, and achieve superior results. Every day, our team supports the development of technologies and processes that advance humanity. Helping our customers reach their goals is our primary focus—and our success is measured by the results we deliver for them.

We’re seeking a Director of Product Management to lead the strategy, portfolio, and execution for our Data & Content Platform, and Data Products powering the Lexis Nexis Intellectual Property patent analytics business. You will own multiyear vision and outcomes for content ingestion, normalization, knowledge/metadata platforms, and commercial data products (APIs, feeds, datasets) that enable research, analytics, and decisioning for IP professionals, R&D leaders, and legal teams worldwide.

You will manage a team of product leaders and SME’s, partner with Engineering, Data Science, and collaborate with Go-to-Market teams to deliver trusted, scalable, and cost-efficient data capabilities.

• Portfolio leadership:
  End to end strategy for patent and related content: ingestion, ETL/ELT, enrichment, normalization, and surfacing across apps and APIs.
• Content platform:
  Roadmap for data lake/lakehouse, knowledge graph/ontology, metadata services, schema governance, data contracts, and lineage.
• Data products:
  Commercial datasets, bulk exports, APIs, event streams, and partner integrations, including pricing/packaging and SLAs.
• Data quality & trust:
  Frameworks for accuracy, completeness, coverage, freshness, deduplication, and explainability.
• People & org:
  Hire, develop, and lead PM managers/ICs; set standards and practices for platform and data product management.

• Define a 3 year strategy and annual roadmaps for Data Management, Content Platform, and Data Products, aligned to LN IP objectives.
• Lead portfolio investment decisions and business cases; optimize build/buy/partner choices (data sources, tooling, and services).
• Establish platform reusability and common services (ingestion, entity resolution, translation/OCR, classification alignment) to accelerate delivery across products.

• Build executive relationships with strategic customers and partners; translate complex data needs into clear platform and product outcomes.
• Track global IP office content (e.g. bibliographic, full text, legal status, citations, assignments, family, prosecution/litigation signals) and incorporate WIPO/EPO/USPTO updates into roadmaps.
• Evaluate the competitive landscape for IP data providers, enrichment vendors, and infrastructure platforms; identify differentiation in quality, coverage, latency, and TCO.

• Own requirements for multi-jurisdiction ingestion (e.g., WIPO, EPO, USPTO, CNIPA, JPO, KIPO, and others), including file formats, schedules, rate limits, and licensing.
• Drive normalization and enrichment (assignee/inventor disambiguation, entity resolution, family building, citations, classifications IPC/CPC alignment, multilingual translation/OCR).
• Partner with Engineering on reliable, observable pipelines (monitoring, alerting, retries, backfills); set targets for throughput, latency, and recovery.
• Advance knowledge graph/ontology capabilities; govern schema evolution, versioning, and compatibility.
• Implement data governance: lineage, cataloguing, access controls, retention, and provenance (source, license, timestamp); uphold security and compliance (e.g., SOC 2, ISO 27001) and license obligations.

• Define and grow external data products (APIs, datasets, feeds, events) and internal services that power applications, search, and analytics.
• Own pricing/packaging, SLAs, quotas, and usage metering; partner on GTM, enablement, and strategic deals/renewals.
• Establish product analytics and telemetry to drive adoption, reliability, and cost efficiency; lead A/B and partner pilots for new offerings.

• Lead governance for content rights, licensing, and provenance; ensure compliant usage and redistribution.
• Create policies and review forums for Responsible AI and data ethics (bias, explainability, human override workflows) in enrichment and derived data.

• Set OKRs, competency frameworks, and career paths; scale best practices in data/platform product management.
• Foster a culture of customer obsession, reliability, and continuous improvement across platform and data teams.

• Freshness & Coverage: % of jurisdictions meeting ingestion SLAs; % coverage of bibliographic/full text/legal status; average lag from source release.
• Quality & Trust:
  Entity resolution F1, deduplication rate, classification alignment accuracy, translation/OCR accuracy, lineage completeness.
• Reliability & Cost:
  Pipeline success rate, MTTR, on time backfills, cost per…