Mining Optimisation Analyst
Employment Info
JOB SUMMARY
Requirements
- Proven experience applying statistical analysis, including correlation analysis, multivariate regression, distributions, significance testing, variance analysis, and predictive modelling, to real-world industrial or operational problems.
- Strong proficiency in SQL and Python for data extraction, transformation, validation, statistical analysis, analytical model development, and automation.
- Experience using Python data science libraries such as pandas, numpy, scipy, scikit-learn, and statsmodels to analyse large operational datasets and develop analytical solutions.
- Practical experience working with large-scale operational datasets using Databricks, PySpark, Snowflake, MS SQL Server, or enterprise data lakes.
- Experience developing and maintaining analytical datasets, data pipelines, and semantic models supporting operational reporting and advanced analytics.
- Ability to translate complex analytical findings into clear, business-ready insights through Power BI, Python visualisation libraries such as seaborn and plotly, or similar platforms.
- A tertiary qualification in Data Science, Statistics, Mathematics, Computer Science, Engineering, Mining Engineering, or a highly quantitative discipline.
- Three to five years of experience in mining analytics, operational analytics, industrial analytics, data science, quantitative analysis, or a related analytical discipline.
- Demonstrated history of delivering measurable business improvements through data-driven investigations and analytical problem solving.
- Experience analysing large operational datasets to identify performance drivers, validate improvement opportunities, quantify business impacts, and support operational decision-making.
- Experience partnering with operational stakeholders to identify opportunities, influence decision-making, and implement sustainable business improvements.
Responsibilities
- Perform rigorous statistical analysis, including multivariate correlation analysis, regression modelling, statistical significance testing, variance analysis, anomaly detection, and predictive modelling, to identify and validate operational improvement opportunities.
- Build, maintain, and refine advanced analytical, statistical, and predictive models, clean analytical datasets, and operational decision-support tools that enable proactive, data-driven decision-making.
- Model and analyse operational parameters such as cycle times, payload variance, delay distributions, match factors, drilling performance, equipment utilisation, and operational variability to uncover performance improvement opportunities.
- Design statistical frameworks and experimental baselines to evaluate continuous improvement outcomes and provide insights into operational performance issues, ensuring performance gains are statistically significant and sustainable.
- Partner with operational control rooms, operational stakeholders, and technology specialists to convert statistical findings into clear, pragmatic recommendations and execution paths.
- Ensure data integrity, model accuracy, and robust data management standards across cloud data platforms such as Databricks and downstream tools.
- Support the identification, prioritisation, implementation, and validation of operational improvement initiatives through rigorous performance measurement and benefits tracking.
- Collaborate with Mining Operations, Technical Services, Maintenance, Autonomous Operations, and Technology teams to identify opportunities, investigate performance constraints, and deliver sustainable business improvements.
Desired Qualifications
- Experience working within mining, resources, manufacturing, logistics, fleet management, autonomous systems, or other operational technology environments is highly desirable.
- Exposure to mining operational technologies, including Autonomous Haulage Systems, autonomous drill systems, fleet management platforms such as MineStar, Modular, and Jigsaw, equipment telemetry systems, machine guidance systems, or related mining technology environments is highly desirable.
- Exposure to fleet telemetry, equipment positioning systems, geospatial analytics, machine guidance systems, or spatial data analysis is highly desirable.
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