An exciting opportunity to join an international gold mining group as the Geoscience Data and Automation Lead in London. Our client is looking for candidates who can combine a strong geoscience background with exceptional data engineering skills.

Our client is strengthening its enterprise management of exploration and geoscience data to accelerate discovery, improve decision quality, and enable advanced analytics and AI.

As part of this journey, they are recruiting a Geoscience Data & Transformation Lead , with accountability for ensuring that exploration and geoscience data is not only governed and secure, but structured, historised, and analytics-ready, enabling advanced geological interpretation, data science, and AI-driven exploration workflows, covering drilling, sampling, geological, and geospatial data.

The role plays a critical part in bridging geoscience domain knowledge with data engineering and data science, ensuring that the client can progressively leverage predictive analytics, machine learning, and automation in exploration activities. The position will manage up to 5 people in the team.

Key responsibilities

1. Geoscience Data Product Ownership & Governance

• Own the end-to-end lifecycle of exploration and geology data, including drilling, sampling,assays, logging, and geospatial datasets.

• Define and enforce data standards, naming conventions, validation rules, and QA/QCautomation for all exploration sites.

• Act as Data Expert for geoscience datasets within the client’s enterprise data governance model.

• Ensure exploration datasets are documented, catalogued, and governed in collaboration with theData & Insights team (e.g. data catalogue, lineage, ownership).

2. Analytics & AI Readiness

• Creating high value Data Products for mine exploration use cases driving the organisation towards Ai- readiness, an ability to translate exploration challenges into modelling problems and guide data scientists in building robust, production-grade AI solutions.

• Ensure exploration datasets are structured to support geostatistics, probabilistic modelling, and machine learning pipelines (feature engineering, model lifecycle, monitoring),

• Strong understanding of 3D geological modelling workflows, block modelling principles, and spatial data structures used in exploration environments,

• Ensure exploration datasets are structured, historised, and analytics-ready,

• Contribute to the definition of future-state exploration analytics capabilities.

3. Exploration Data Systems & Architecture

• Own and oversee Geoscience Data Management Systems (GDMS), including central geology databases, drilling and sampling repositories, interfaces with GIS and geological modelling tools in collaboration with Enterprise Architecture.

• Ensure systems are scalable and fit-for-purpose, aligned with enterprise IT standards, cyber-secure and resilient.

• Partner with IT teams to modernize legacy exploration data systems, integrate GDMS with thecentral data platform, reduce manual data handling and duplication.

4. Data Capture, Validation & Quality Assurance

• Design and supervise data capture workflows from field to system: site geologists, samplers, contractors.

• Implement automated QA/QC processes to ensure data accuracy, completeness, and consistency.

• Coordinate with: Regional Exploration QA/QC Managers, Site Data Stewards / Field Geologists, GISGeologists, to ensure data is validated at each stage before use in interpretation or modelling.

5. Collaboration & Stakeholder Enablement

• Act as a trusted partner to Exploration and Geology leadership.

• Translate business needs into data and system requirements.

• Drive adoption through training, documentation, and continuous engagement with end users.

• Ensure new processes and systems are embedded into daily exploration workflows.

6. Success in 24 Months Looks Like:

• Core exploration data model standardised across all sites

• AI-assisted targeting pilot delivered and adopted

• Reduction in time from drilling to validated geological insight

• Exploration datasets fully integrated into enterprise data platform

• Clear roadmap for predictive exploration

Experience & Qualifications

Education:

• Master’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related technical field. Geoscience or equivalent knowledge is a must.

Geoscience Data, Analytics & Platforms

• Strong Background in ML/AI related field with hands-on experience

• Strong understanding of Geospatial data & Systems.

• Proven ability to design and operate analytics-ready geoscience data models supporting geological interpretation, resource modelling, and exploration performance analysis.

• Exposure to advanced analytics, Geostatistics, or AI-assisted imaging or modelling with spacial data.

Systems, Architecture & Data Platforms

• Proven experience managing or overseeing Geoscience Data Management Systems (GDMS) and related exploration platforms.

• Strong understanding of data integration, historization, data quality automation, metadata, and lineage.

• Experience with enterprise data platforms (cloud preferred) and familiarity with GIS and geospatial systems.

• Ability to work effectively with enterprise architecture, data engineering, cybersecurity, and infrastructure teams.

Data Governance & Quality

• Strong understanding of data governance, stewardship, and ownership models.

• Experience defining data standards, validation rules, QA/QC automation, and certifying trusted datasets.

• Ability to balance governance and usability to enable analytics while ensuring trust and compliance.

Leadership & Delivery

• Demonstrated experience leading or coordinating small specialist teams (direct or matrix).

• Strong stakeholder management across Exploration, IT, and Data & Analytics.

• Proven ability to manage multiple priorities across sites while delivering in a transformation context.