Chief Specialist, Accelerating discovery to development (Exploration & Resource Development Business Partner)

About the Role
The Chief Specialist, Accelerating Discovery to Development transforms how the organization applies technology to mineral exploration, accelerating the journey from exploration insight to scientifically validated, decision-ready evidence. This role connects frontier technologies in automation, advanced sensing, Data & AI, subsurface analytics, and laboratory science to create faster, more reliable and scalable exploration capabilities. As an enterprise technical authority, the role shapes how emerging technologies are selected, validated, and applied across the exploration lifecycle. It brings together field acquisition, laboratory validation, and digital intelligence to shorten learning cycles, improve confidence in exploration findings, reduce avoidable rework, and strengthen the technical basis for investment and portfolio decisions. The role creates additional value by translating proven approaches from Oil & Gas, space exploration, OEMs, academia, and research into practical mineral exploration applications. Its accountability is how exploration technology is applied and validated, rather than ownership of exploration outcomes, laboratories, digital platforms, or approval decisions.

What You Will Deliver
Exploration Technology Leadership

• Shape the enterprise exploration technology roadmap to accelerate discovery and validation through automation, advanced sensing, AI-enabled targeting, and intelligent drilling.
• Translate emerging technologies into deployable exploration capabilities, selecting solutions based on scientific validity, technology readiness, field applicability, and potential business value.
• Build the organization’s technical advantage by identifying and adapting relevant technologies and practices from Oil & Gas subsurface science, space exploration, OEMs, academia, and research institutions.

Data, AI & Subsurface Intelligence

• Advance AI-enabled exploration by integrating geological, geophysical, geochemical, and remote-sensing information to strengthen subsurface interpretation, target ranking, and uncertainty management.
• Apply advanced analytics, including predictive modeling, deep learning, physics-informed AI, and probabilistic approaches, to increase the speed and confidence of exploration insights.
• Establish confidence-based decision metrics that make uncertainty, reproducibility, and learning rate visible alongside conventional exploration measures.

Accelerated Field-to-Lab Validation

• Transform the field-to-lab validation cycle by connecting sensing, drilling, and sampling with high-throughput analytical methods, automated core analysis, and digital sample traceability.
• Accelerate independent and reproducible validation of exploration findings, reducing analytical turnaround, rework, and delays between initial observation and decision-ready evidence.
• Strengthen approval readiness by establishing fit-for-purpose certified analytical technologies, traceable data, and scientifically defensible methods that enable efficient technical review, external reporting, and portfolio decisions.

Integrated Exploration Operating Model

• Design scalable field-lab-digital workflows that connect field acquisition, laboratory analysis, and AI-enabled interpretation into a coherent exploration technology model.
• Enable greater exploration productivity through automation, digital twins, sensor fusion, autonomous or remote technologies, and AI-assisted decision workflows.
• Align laboratory capacity, automation, and digital capability with exploration ambition, enabling technology solutions to scale as exploration activity evolves.

Technology Innovation & Value Realization

• Evaluate frontier technologies for practical exploration value, balancing scientific robustness, technology readiness, deployment constraints, cost, risk, and scalability.
• Accelerate transfer of proven cross-industry innovation into mineral exploration, particularly in autonomous systems, subsurface analytics, remote sensing, and extreme-environment instrumentation.
• Demonstrate measurable technology value through shorter learning and validation cycles, lower re-testing and re-sampling, improved target confidence, and more efficient exploration workflows.

What Success Looks Like

• Shorter field-to-validated-result cycle time, enabling exploration evidence to reach technical and portfolio decisions faster.
• Reduced laboratory turnaround time and avoidable re-analysis, with stronger first-time quality, traceability, and reproducibility of critical results.
• Higher confidence in drill-ready targets, supported by integrated data, explicit uncertainty measures, and scientifically defensible validation.
• Faster progression from exploration concept to drill-ready target through effective application of automation, AI, and advanced sensing technologies.
• Improved exploration efficiency, demonstrated through reduced cost, re-sampling, re-testing, and decision latency attributable to technology-enabled workflows.
• Successful adoption and scaling of high-value technologies, including AI-enabled exploration and relevant cross-industry innovations.

Minimum Qualifications

• Advanced degree in Geoscience, Geophysics, Mining Engineering, or a related discipline; MSc or PhD preferred.

Experience

• 20+ years of relevant experience in mineral exploration, subsurface science, or adjacent Oil & Gas exploration domains.
• Demonstrated experience applying Data & AI, automation, advanced sensing, or subsurface analytics in exploration or comparable technical environments.
• Experience with advanced analytical laboratories, mineral characterization, high-throughput testing, certified analytical methods, or regulatory acceptance processes.
• Experience applying AI-enabled QA/QC, anomaly detection, confidence estimation, or uncertainty analysis, combined with a strong understanding of field exploration constraints.

Skills That Matter

• Mineral exploration technology and subsurface science
• Advanced sensing, drilling, and field technologies
• Data, AI, and predictive subsurface analytics
• Laboratory analytics and mineral characterization
• Scientific validation, QA/QC, and uncertainty quantification
• Technology assessment, qualification, and scaling
• Exploration workflow optimization and value realization
• Cross-industry technology transfer
• Executive-level technical advisory and stakeholder influence
• Cross-disciplinary collaboration across geoscience, digital, engineering, R&D, OEMs, and academia