Data Analyst, Wet Plant

About the role

First Quantum is hiring a Data Analyst for its wet plant, reporting to the Senior Project Engineer. The analyst works with business and operational leaders to turn data and analytical tools into actionable insights, using statistical, algorithmic, mining and visualization techniques to model complex process problems and support senior leadership decisions.

Key responsibilities

• Monitor, analyze and optimize plant performance data to improve recovery, throughput, concentrate quality and efficiency, and identify bottlenecks and improvement opportunities.
• Provide technical support and recommendations to Operations and Maintenance teams.
• Develop, maintain and improve metallurgical databases, process reports, dashboards and performance statistics.
• Perform process modelling, statistical analysis, test work and process evaluations, and develop mass balances, PFDs and P&IDs.
• Build and maintain online process monitoring dashboards and reporting tools, and ensure data quality and integrity.
• Support the deployment, monitoring and improvement of analytical and predictive models, and communicate findings to stakeholders.
• Support metallurgical accounting, lead process improvement projects, and perform ad hoc cover when required.

Required profile

• Bachelor's degree in Metallurgy or Mineral Processing; degrees in Data Science, Computer Science, Statistics or Process Control are an added advantage.
• Training or certification in Machine Learning, AI, Data Analytics or Process Optimization is advantageous.
• All qualifications must be certified by the Zambia Qualifications Authority (ZAQA).
• Minimum 5 years in a large-scale mineral processing plant, covering crushing, grinding, flotation, thickening and metallurgical accounting.
• Minimum 2 years applying data analytics, statistical analysis and visualization to operational decisions.
• Experience in process modelling, mass balancing and metallurgical performance analysis.

Required skills

• Data analysis and programming tools such as Python, SQL, Power BI or similar.
• Statistical techniques including regression, multivariate analysis, process control monitoring, machine learning and predictive analytics.
• Process dashboards, production reporting and performance monitoring tools.
• Familiarity with JKSimMet, MetSIM or similar simulation tools, and with plant historians, SCADA, PI System and control systems is an added advantage.