Superintendent, Process Mineralogy and Geometallurgy

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Job description:

Purpose of the role

This job scope has two major components, namely geometallurgy and process mineralogy. Geometallurgy involves using data from multiple data sources (mineralogy, hardness, RC drill penetration rates and other) to develop predictive models for mine planning to inform blending strategies, process optimisation and long-term budgets and forecasts. Kansanshi has complex mineralogy with gaps in terms of the applicability of the ideal modern geometallurgy concept, so the candidate will be expected to make the most of the available information, while establishing appropriate workflows to improve the quality of data for long term benefit where required. The role will play a key part in co-ordinating the efforts of various specialists in other departments (Geology, Mine Planning, Mining Operations, Processing, Environmental) in the challenging, dynamic work environment of a complex copper and gold processing plant.

Process Mineralogy involves Auto-SEM analysis (using the TIMA) of daily and weekly plant samples to identify opportunities for further recovery and concentrate quality improvements and to assist with troubleshooting. The process mineralogy team consisting of metallurgists, data analysts and technicians, who mostly perform routine analyses, but need occasional guidance for interpretation of unexpected results/ and development of new workflows or non-routine analyses.

Job & Candidate Requirements 

The individual should have a strong technical specialist ability (in metallurgy, mineralogy, geology or data science) and be passionate about applying his/ her ability within a team to solve problems. They will work closely with process engineers, process operations and other disciplines within the mining value chain to improve mine plans to maximise copper output, optimise metal recoveries, and improve overall plant performance.

Key Responsibilities:

• Lead the development, implementation, and continuous improvement of geo-metallurgical models integrating geological, mineralogical, and metallurgical data and feeding essential information into mine plans and production budgets/forecasts.
• Perform/ oversee/ coordinate diagnostic metallurgical and mineralogical investigations to support troubleshooting and optimisation.
• Conduct process mineralogical studies (e.g. TIMA, optical microscopy) to characterise ore and identify key metallurgical drivers.
• Develop and implement strategies to enhance the accuracy and efficiency of mineralogical analysis.
• Oversee/ maintain databases, ensuring accurate data capture and perform or work with data analyst to develop supervised machine-learning models to support optimisation.
• Collaborate with process engineers and other members of the metallurgical department to translate ore characteristics into predictive performance models and processing strategies.
• Collaborate with metallurgists and processing engineers to identify and address plant performance variability related to ore characteristics and optimize metal recoveries and improve overall plant performance.
• Identify opportunities for innovation within the mineralogical analysis process and implement appropriate solutions.
• Provide technical guidance and mentorship to geologists and metallurgists in geometallurgical principles, sampling, and data integration.
• Stay abreast of advancements in mineralogical analysis techniques and incorporate relevant developments into the laboratory's workflow.

Qualifications, Experience & Skills Required:

• Bachelor’s degree in Geology, Chemical Engineering, Metallurgy, or related discipline; a postgraduate qualification in Process Mineralogy or Geometallurgy is an advantage.
• Preferably 5–10 years’ experience in mining or mineral processing environments, with strong exposure to process mineralogy and/or geometallurgy.
• Proficiency in problem solving and critical thinking.
• Effective communication and interpersonal skills.
• Understanding of extractive metallurgy principles.
• Demonstrated ability to lead and motivate employees/ colleagues.
• Experience with process engineering concepts and practices.

Key Competencies

• Mathematics, chemistry (or physics), mass balancing, mineral balancing.
• Ability to translate and effectively communicate complex data into actionable operational insights.
• Ability to move forward in the short-term using available insight (with associated high uncertainty), despite having imperfect/ incomplete data, while working in the background to improve the data quality for future reduction in uncertainty.
• Collaborative and mentoring approach to knowledge sharing.

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