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Working with us is not like working anywhere else, which is why we recruit people who will take a bolder, smarter approach to spot opportunities, solve problems and deliver results.

Our culture is all about encouraging you to think independently and to challenge convention to deliver the best result. That’s how we continue to achieve extraordinary things in extraordinary locations.

Job description:

Job title: Superintendent, Digital Innovations  

Site:  Kansanshi Mining Plc.  

Department:  IT & Digital  

Section: AI Innovations  

Position reports to:  IT & Digital Manager 

Purpose

The Superintendent is responsible for identifying, prioritising, and embedding AI-driven solutions including predictive analytics, machine learning, generative AI, intelligent automation, computer vision, and operational optimisation solutions that support mining production, maintenance, safety, logistics, operational efficiency, and employee productivity. The role bridges deep technical AI expertise with mining domain knowledge, working closely with Mine Operations, Maintenance, Processing, Safety, and IT leadership to ensure AI initiatives deliver measurable business value. The Superintendent champions responsible AI adoption, builds internal AI capability, and ensures all deployed solutions meet governance, compliance, and ethical standards within the mining context.

Key Responsibilities

• Develop and execute the mine site AI strategy, aligning AI initiatives with operational priorities in mining, processing, maintenance, and safety — ensuring AI investments deliver tangible production and cost outcomes.
• Lead the identification, scoping, and business case development for high-value AI use cases across mining, processing, smelter and support services.
• Oversee the full lifecycle of AI solutions from data acquisition and model development through to production deployment, performance monitoring, and continuous improvement — using MLOps/LLMOps best practices in mine environments.
• Direct the integration of AI and machine learning models with operational technology (OT) systems including SCADA, fleet management systems (FMS), mine planning tools, historians, and condition monitoring platforms.
• Lead and mentor a team of AI Engineers, Data Scientists, and AI Specialist, fostering a high-performance culture focused on innovation, collaboration, and skills development within the AI section.
• Partner with Mine Operations, Maintenance, Geology, Survey, Processing, and Safety teams to embed AI-driven insights into daily workflows, planning cycles, and decision-making processes.
• Oversee the development and deployment of predictive maintenance models for critical mining assets including shovels, trucks, conveyors, mills, pumps, and compressors — leveraging vibration, thermal, and operational data.
• Drive the implementation of computer vision across mining, processing, smelter and safety to deliver operational improvements
• Champion the use of AI-powered production optimisation tools including real-time fleet dispatch, drilling and blasting intelligence, ore body modelling, and pit-to-plant integration using digital twin technologies.
• Manage vendor relationships and technology partnerships for AI platforms, cloud services, and specialist AI solution providers relevant to the mining sector.
• Develop and manage the AI section budget including capital expenditure for AI infrastructure, operational costs, licencing, and training — ensuring spend delivers measurable return on investment.
• Drive AI literacy and adoption across the mine site through structured training programmes, workshops, and targeted enablement sessions for operational and technical teams.
• Produce executive-level reporting on AI programme performance, value delivered, risks, and the forward roadmap for presentation to site and group leadership.
• Ensure all AI solutions comply with applicable regulatory requirements, environmental standards, and First Quantum Minerals data governance and cybersecurity policies.
• Stay abreast of emerging AI research, mining technology trends, and industry best practices — translating relevant advances into actionable opportunities for the site.
• Actively research industry trends and anticipate the issues, risks and opportunities that stem from these
• Maximize the organizational benefits that stem from digital products and services
• Establish a customer success strategy to drive the achievement of new digital products and services
• Demonstrate and communicate the added value of the new digital products and services to organization’s performance

Qualifications

• Bachelor’s degree in computer science, Software Engineering, Information Systems, Mathematics, or a related analytical / engineering discipline.
• Master's degree or postgraduate qualification in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or Mining Engineering with an AI specialisation will be a strong advantage.
• Certification in AI/ML platforms (e.g. Microsoft Azure AI Engineer, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer) is highly desirable.
• Candidate must have deep knowledge in mining across key areas being MTS, Mining Operations, Processing and Smelter operations.
• Project management certification (PMP, PRINCE2, or Agile/Scrum) is an added advantage.
• A background in Applying AI in Mining Engineering, Metallurgical Engineering, or Electrical / Instrumentation Engineering is particularly valued
• All academic qualifications and results must be verified by the Zambia Qualifications Authority (ZAQA).

Experience

• Minimum of 10 years’ experience delivering Data and AI initiatives across mining operations, including mine production, mine maintenance, Mine Technical Services (MTS), processing, and smelter environments.
• Minimum 10 years of experience in AI/ML engineering or data science, with at least 3 years in a leadership or supervisory role overseeing AI programmes in an industrial, resources, or mining environment.
• Experience converting ambiguous business requirements or AI use cases into clearly defined, measurable, and actionable deliverables.
• Demonstrated track record of delivering AI solutions in operational technology (OT) environments including integration with SCADA, DCS, historians (e.g. OSIsoft PI, Aveva), and fleet management systems (e.g. Modular, Wenco, Jigsaw).
• Deep expertise in machine learning, deep learning, and time-series modelling for predictive maintenance, anomaly detection, and equipment failure prediction using vibration, thermal, and process data.
• Hands-on experience with computer vision applications including object detection, image classification, and video analytics in industrial or outdoor environments.
• Proficient in Python and AI/ML frameworks including scikit-learn, TensorFlow, PyTorch, and XGBoost; familiarity with signal processing libraries (SciPy, librosa) is advantageous.
• Experience building and deploying LLM-based applications including AI assistants, RAG pipelines, and agentic AI workflows for operational knowledge management.
• Strong experience with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) and MLOps tooling (MLflow, Kubeflow, Azure ML, SageMaker) in production environments.
• Working knowledge of industrial data protocols and standards including OPC-UA, MQTT, Modbus, and real-time data historian integration.
• Experience with geospatial data processing, mine planning software integration, or ore body modelling environments (e.g. Vulcan, Leapfrog, Datamine) is a significant advantage.
• Strong SQL proficiency and experience with big data processing frameworks (Spark, Hive, Databricks) for large-scale sensor and operational data.
• Exposure to digital twin platforms, asset performance management (APM) systems, and real-time process optimisation in mining or heavy industry.
• Familiarity with responsible AI principles including fairness, explainability, privacy-preserving techniques, and AI governance frameworks applicable to safety-critical environments.
• Member of ICTAZ or EIZ will be an added advantage.
• Experience working in mining operations environments is a must.

BEHAVIOURAL TRAITS

• Strong analytical and problem-solving abilities.
• Visionary leadership with the ability to translate AI strategy into practical, high-impact solutions within a complex mine operational environment.
• Strong communicator able to convey AI concepts, risks, and value to diverse audiences including mine operators, maintenance crews, engineers, and senior executives.
• Collaborative and cross-functional mindset — comfortable building relationships and influence across Operations, Maintenance, Safety, Geology, and IT disciplines.
• Operational pragmatism — balances innovation ambition with the realities of production continuity, safety requirements, and resource constraints in a mining context.
• Data-driven decision-making with an ability to challenge assumptions and use evidence to build compelling cases for AI investment and prioritisation.
• High standards for model quality, output reliability, and documentation — particularly for AI applied to safety-critical or production-critical use cases.
• People developer with a genuine commitment to upskilling team members and building lasting AI capability within the mine organisation.
• Results-oriented with strong programme management discipline — able to deliver multiple AI initiatives concurrently within budget, scope, and timeline.
• Ethical orientation and commitment to responsible AI practices, particularly in safety monitoring, workforce management, and environmental compliance applications.
• Resilient and adaptive — able to navigate ambiguity, reprioritize under operational pressure, and maintain momentum on strategic AI objectives.

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