Product Manager - Digital Twins
Listed on 2026-06-05
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IT/Tech
Data Science Manager, Data Engineer
Location: Aberdeen City
Our purpose is to deliver energy to the world, today and tomorrow. For over 100 years, bp has focused on discovering, developing, and producing oil and gas in the nations where we operate. We are one of the few companies globally that can provide governments and customers with an integrated energy offering. Delivering our strategy sustainably is fundamental to achieving our ambition to be a net zero company by 2050 or sooner!
Role OverviewThe Staff Digital Twin Product Manager leads the strategy, development, and delivery of enterprise digital twin capabilities across global upstream assets. This role sits at the intersection of product management, data platforms, digital engineering, and asset operations, driving scalable, cloud‑native digital twin platforms that deliver measurable outcomes across safety, reliability, efficiency, and cost.
This is a staff‑level product leadership role, accountable not only for delivery, but for product adoption, platform sustainability, and realized business value. The role combines deep technical expertise in data modeling, contextualization, and integration with strong commercial foresight and cross‑organizational influence.
Product Strategy & Vision- Define and own the product vision, roadmap, and value proposition for the Digital Twin platform and its core capabilities.
- Lead the strategy for data modeling, graph‑based contextualization, and asset virtualization, supporting a wide range of operational and engineering use cases.
- Align product direction with enterprise priorities, including data platform simplification, automation, cost efficiency, and digital transformation.
- Partner with Product, Architecture, and Engineering to evolve digital twin capabilities for reuse across additional business areas.
- Own the end‑to‑end product lifecycle: conceptualisation, delivery, deployment, adoption, and continuous improvement.
- Lead Agile product delivery, including backlog prioritization, release planning, dependency management, and risk mitigation.
- Provide technical guidance and delivery leadership to squads, ensuring alignment across data, platform, and visualization work streams.
- Supervise twin deployment, enhancement, and sustainment, ensuring solutions transition cleanly into operations.
- Act as the technical liaison between business and engineering, shaping solutions built on data architectures, including:
- Cloud‑native data platforms (e.g., Databricks)
- Graph and contextual data models
- APIs and event‑driven integrations
- SAP, ALIM, PI/IoT, engineering, and 3D data sources
- Lead the creation of a cost‑effective, reliable, reusable digital twin platform supplying data to 1D / 2D / 3D / 4D visualization and analytics.
- Define and enforce data standards, governance, quality, and security frameworks.
- Coordinate data remediation and improvement plans with technology and data partners.
- Engage directly with asset, engineering, and operational partners to:
- Understand use cases and data requirements
- Identify value opportunities and success measures
- Drive adoption and sustained usage
- Communicate roadmap progress, delivery plans, value realized, and costs to peers and senior leadership.
- Build strong, positive governance and collaboration models across product owners, product managers, and portfolio managers.
- Identify, validate, and track benefits and value realization, including efficiency gains, cost reduction, and risk mitigation.
- Define and monitor product KPIs tied to adoption, performance, reliability, and business impact.
- Apply strong commercial judgment to prioritise initiatives that improve ROI and strategic value.
- Support cases, funding strategies, and investment decisions.
- Bachelor's degree or equivalent experience in Engineering, Computer Science, Data Science, or related subject area.
- Strong experience in data, digital, or technology delivery in complex, global environments.
- Experience leading and developing teams, preferably with data or digital twin relevance.
- Deep understanding of:
- Data models, data standards, and contextualization
- Data…
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