Product Manager – Drilling Solutions AI

The Product Manager, Drilling Solutions AI owns and executes the product roadmap for artificial intelligence capabilities across the Nabors Drilling Solutions technology portfolio. This hands-on role combines product management with rapid solution development: the position identifies high-value drilling and operational use cases, validates them directly by building proofs of concept and working prototypes, and translates validated concepts into scalable product requirements for Engineering and Data Science teams. The role works across Product Management, Engineering, Operations, Commercial, and customer stakeholders to deliver AI-enabled solutions that improve drilling performance, operational efficiency, safety, and customer outcomes. The position applies responsible AI, security, and product governance throughout discovery, prototyping, pilot deployment, commercialization, and continuous improvement.

AI Product Strategy & Roadmap Execution

• Develop and maintain the NDS AI product roadmap for assigned drilling technology platforms and use cases, aligned with business objectives and customer needs.

• Evaluate customer problems, operational workflows, market signals, and technical feasibility to define practical AI product opportunities.

• Prioritize initiatives and backlog items using expected customer value, operational impact, implementation effort, risk, and measurable success criteria.

• Prepare business cases, product briefs, investment recommendations, and product performance updates for leadership review.

Hands-On AI Prototyping & Solution Development

• Personally build proofs of concept, working prototypes, demonstrations, and lightweight AI solutions to validate user needs, data readiness, model behavior, workflow fit, and business value before scaling.

• Use approved development tools, cloud services, APIs, data platforms, low-code tools, scripting languages, and AI frameworks to assemble and test end-to-end solutions.

• Create prompt flows, retrieval and grounding approaches, evaluation datasets, user interfaces, automations, integrations, and analytics needed for functional prototypes.

• Test prototypes with users, document findings and technical limitations, iterate quickly, and recommend whether to stop, refine, pilot, or transition the solution to Engineering.

• Maintain prototype code, configuration, documentation, and reusable components in approved repositories and follow cybersecurity, data-handling, architecture, and responsible AI requirements.

Customer Discovery, Product Definition & Commercialization

• Lead customer and field discovery to understand drilling workflows, decision points, pain points, and adoption barriers, then convert findings into clear product outcomes.

• Define user journeys, product requirements, acceptance criteria, release scope, value propositions, demonstrations, and adoption plans for AI-enabled capabilities.

• Partner with Commercial and Operations teams on pilots, customer engagements, product positioning, enablement materials, and go-to-market readiness.

• Measure whether released capabilities deliver expected improvements in drilling performance, operational efficiency, risk reduction, and customer experience.

Product Delivery & Cross-Functional Execution

• Coordinate cross-functional delivery from discovery and prototype through pilot, production release, adoption, and lifecycle improvement.

• Manage requirements, backlog priorities, dependencies, delivery risks, release readiness, user acceptance, documentation, and adoption activities.

• Work directly with Engineering, Data Science, Architecture, Cybersecurity, Operations, Support, and Commercial teams to resolve tradeoffs and maintain delivery momentum.

• Provide clear decisions, status, risks, and outcome measures to stakeholders and escalate issues that require leadership action.

Data, Model Performance & Product Analytics

• Assess data availability, quality, lineage, operational context, and limitations during discovery and prototype development.

• Define and monitor product, model, adoption, reliability, and business-value measures for prototypes, pilots, and production capabilities.

• Perform structured evaluation of AI outputs, including accuracy, relevance, reliability, latency, failure modes, and user feedback.

• Use quantitative and qualitative insights to improve prototypes, refine requirements, prioritize enhancements, and support product decisions.

Responsible AI, Security & Product Governance

• Apply responsible AI, cybersecurity, privacy, legal, records-management, and data-governance requirements throughout the product lifecycle.

• Document intended use, limitations, evaluation results, human oversight, access controls, data sources, and operational risks for AI solutions.

• Partner with appropriate control functions to address security, compliance, model-risk, and operational-readiness concerns before pilots or production release.

• Support user enablement, feedback channels, product monitoring, incident response, and controlled change management for deployed capabilities.

Required

• Bachelor's degree in Engineering, Computer Science, Data Science, Information Systems, Business, or a related discipline, or an equivalent combination of education and directly relevant experience.

• 4–8+ years of experience in product management, software or digital solution delivery, data or AI products, drilling technology, or a related technical role.

• Demonstrated experience owning product discovery, requirements, backlog priorities, delivery coordination, and measurable product outcomes.

• Demonstrated hands-on experience building and testing proofs of concept, prototypes, automations, analytics, or AI-enabled solutions using modern development, cloud, data, API, scripting, or low-code tools.

• Working knowledge of AI and machine-learning solution patterns, data requirements, evaluation methods, integration concepts, and responsible AI considerations.

• Ability to translate operational and customer problems into testable hypotheses, functional prototypes, product requirements, and implementation recommendations.

• Effective written and verbal communication with technical, operational, commercial, customer, and leadership stakeholders.

Preferred

• Master's degree in Business Administration, Engineering, Computer Science, Data Science, or a related discipline.

• Experience developing AI, machine-learning, generative-AI, predictive-analytics, automation, or decision-support solutions.

• Experience with Python, SQL, APIs, Git-based source control, prompt engineering, retrieval-augmented generation, agentic workflows, model evaluation, or comparable tools and methods.

• Experience with Azure AI services, Azure OpenAI, Microsoft Copilot Studio, Databricks, Snowflake, Power BI, cloud platforms, or comparable technologies.

• Experience in drilling, oilfield services, industrial technology, automation, operational technology or digital operations environments.

• Product management, Agile, cloud, data, or AI-related certification.

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