About The Role

The Product Manager, Knowledge Platform drives strategy and development of Lightcast’s knowledge-layer capabilities across taxonomy, ontology, analytics, and machine learning-powered data products. The role focuses on understanding customer problems, evaluating data and technical opportunities, and translating ambiguous challenges into clear product direction. Success requires strong product judgement, technical fluency, autonomy, and the ability to connect customer needs with data, AI, and engineering capabilities. *THIS IS A REMOTE POSITION BASED IN UK*

Major Responsibilities

Define the strategy, roadmap, and investment priorities for the knowledge platform.
Lead discovery of new capabilities by identifying customer problems, assessing data feasibility, and validating opportunities.
Translate complex data opportunities into clear product requirements, specifications, quality criteria, and investment decisions.
Partner with engineering, data science, taxonomy, analytics, and commercial teams throughout product development.
Balance customer value, data quality, technical feasibility, and business priorities when making product decisions.
Communicate complex technical and data-product concepts effectively to technical, commercial, and executive stakeholders.
Evaluate developments in AI, analytics, labour market intelligence, and knowledge technologies to identify valuable new opportunities.
Use data and evidence directly to assess quality, explore opportunities, and guide product decisions.

Education and Experience

Education: Bachelor’s degree preferred in a relevant field.
Experience: 3+ years in a relevant role, with demonstrated autonomy and ownership.
Significant experience working in a data-rich environment.
Experience with technical, analytical, or data-focused products, including product strategy, discovery, and/or delivery.
Technical fluency in areas such as data, analytics, AI, machine learning, taxonomy, ontology, or knowledge systems.
Experience working cross-functionally with customers, engineering, data science, product, and commercial stakeholders.
Ability to work directly with data, including exploratory analysis and data-quality assessment.
Strong interest in AI, knowledge representation, labour market intelligence, and the future of work.