Principal Data Science

BHP Career Portal

1d•Hybrid

About The Position

The Principal Data Scientist is responsible for developing and deploying advanced analytics, machine learning, and AI capabilities to solve geoscience problems that accelerate mineral exploration and improve decision quality across the Exploration portfolio. As a member of Exploration’s Data Science team, this role partners closely with geoscientist customers, data scientist peers, and technology partners to transform a diverse array of large, complex exploration geoscience datasets into actionable insights and scalable digital solutions that support discovery and boost geological confidence. The position serves as a trusted technical partner to geoscience customers, helping to identify high-value opportunities, frame complex geoscience-centric problems, and translate exploration challenges into practical analytical solutions. Success in this role requires expertise in mathematics, statistics, machine learning, and AI, supported by strong computer science and programming capabilities to ensure solutions are robust, maintainable, and scalable. Success will depend on effective communication with project partners and delivering solutions endorsed by the customer. The successful candidate will bring substantial experience applying machine learning and AI to physical science problems, combining technical excellence with a strong focus on delivering measurable business outcomes, advancing exploration capabilities, and creating value by working with geoscience exploration customers to improve exploration opportunity selection speed and quality.

Requirements

Over 5 years of experience applying advanced data analytics, machine learning, and AI to physical sciences, preferably for solving large-scale business problems.
Over 5 years of programming experience in physical sciences using Python, C, R, or Java.
Over 3 years of experience programmatically accessing (APIs), integrating, querying, and manipulating large, structured datasets from relational databases (SQL Server, PostgreSQL, Oracle, Snowflake, etc.) with programmatic connections to data sources (e.g., APIs).
Skilled in applying computer science knowledge in redesigning compute configurations to improve code data processing efficiency for cost- and time-effective scaling.
Skilled in using high-performance cloud computing platforms (e.g., Azure Cyclecloud, AWS ParallelCluster, Google Cloud Cluster) to accelerate data processing and analysis.
Skilled in using code management platforms like GitLab, with proven experience collaborating with other developers on large-scale business projects.
Demonstrated experience in working with large data systems in applied ML or AI problems.
Over 3 years of experience leading multidisciplinary scientific teams to deliver impactful results, including project schedule and budget management skills.
Demonstrated experience in explaining complex analytical, machine learning, or AI workflows to non-technical audiences.

Nice To Haves

Postgraduate degree (MSc/PhD) in data science, computer science, statistics, mathematics, or physical sciences.
Experience applying compute-intensive advanced data analytics, machine learning, or AI solutions to mineral exploration geoscience problems such as probabilistic geophysical inversions, mineral prospectivity mapping, automated core logging, remote sensing analysis, or large-scale agentic AI data extraction.
Experience in geospatial analytics, including raster, vector, and 3D data processing, along with validation of spatial or geoscience models considering spatial leakage, uncertainty, sampling bias, spatial autocorrelation, scale of applicability, and ground truthing.
Exposure to probabilistic or Bayesian modelling, particularly the use of prior information, Bayesian updating with new evidence, and interpretation of posterior distributions to quantify uncertainty and support decision-making.
Experience working with geologists, geophysicists, geochemists, or geodynamicist customers or collaborators, particularly in a mining exploration context.
Experience applying modern AI techniques, including generative AI, deep learning, information extraction, or agent-based workflows.
Exposure to earth systems modeling workflows such as plate modeling, mantle convection, landscape evolution, or fluid flow modeling.

Responsibilities

Lead collaboration with geoscience customers and data scientist peers to develop customized data analytics, machine learning, and AI solutions for large-scale exploration geoscience problems across geology, geochemistry, geophysics, geodynamic modeling, remote sensing, and resource modeling.
Lead collaboration with the Exploration Technology team to deploy solutions into production environments, ensuring their sustainability and ongoing use beyond initial development.
Identifying and appropriately applying novel data analytics, machine learning, AI, or computational efficiency opportunities to existing exploration workflows to accelerate the delivery of exploration opportunities through the pipeline and increase the quality of selected opportunities.
Providing technical leadership and assurance to outsourced data science resources supporting exploration teams to ensure technical work is compliant with internal standards and best practices.
Define, set, and assure adherence to exploration data science standards and best practices, including code-based workflows or machine learning and AI for geoscientific problems.
Managing data-science enabling opportunity projects including designing and managing project schedules, budgets, and resourcing.

Benefits

Domestic relocation assistance
New home purchase assistance
Temporary accommodations
Home finding trip
Resettlement allowance
Professional Development
Continuous learning and real opportunities for growth
Fair and competitive compensation
Inclusive Culture
A workplace where everyone feels valued, respected, and free to express their authentic identity