Lead Specialist, Data & Artificial Intelligence Delivery Management

Job description

1. Why This Role Matters The Lead Specialist, Data Science & Analytics drives the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions that improve operational performance, business decision-making, and enterprise productivity across the employer. The role transforms complex business challenges into scalable analytical solutions that generate measurable value across mining, processing, operations, and corporate functions. The role bridges business priorities with technical execution, ensuring that analytical products progress efficiently from concept through deployment and industrialization. By embedding AI and data science capabilities into operational processes, the role accelerates digital transformation and strengthens the employer’s ability to leverage data as a strategic asset. Through technical leadership, governance oversight, and stakeholder engagement, the role ensures analytical solutions are reliable, scalable, compliant, and aligned with enterprise standards, enabling sustainable adoption and value realization across the organization. 2. What You Will Deliver AI & Data Science Solution Delivery • Lead the end-to-end development of machine learning, AI, and advanced analytics solutions that address high-value business challenges. • Deliver scalable, production-ready models that improve operational efficiency, reliability, productivity, and decision-making. • Strengthen analytical capabilities through robust experimentation, model optimization, and continuous performance improvement. Business Value & Use Case Development • Translate business priorities into actionable analytics opportunities, value hypotheses, and implementation roadmaps. • Identify, prioritize, and develop AI and analytics use cases that deliver measurable operational and financial outcomes. • Establish clear success measures, KPIs, and value realization frameworks for analytical initiatives. AI Industrialization & Platform Integration • Enable seamless deployment of analytical models into enterprise systems, operational platforms, and digital environments. • Strengthen model reliability and scalability through effective MLOps practices, lifecycle management, and monitoring standards. • Improve adoption and sustainability of AI solutions by ensuring technical readiness and operational integration. Data Quality & Analytical Foundations • Strengthen trust in analytics through reliable, governed, and high-quality data foundations. • Partner with Data Engineering teams to improve analytical environments, data pipelines, and model readiness. • Ensure data assets meet enterprise requirements for accuracy, integrity, security, and compliance. Responsible AI & Governance • Embed responsible AI principles, governance controls, and quality standards across analytical solutions. • Strengthen transparency, explainability, auditability, and lineage across data science assets and models. • Ensure compliance with enterprise data governance, security, and regulatory requirements. Stakeholder Engagement & Value Realization • Build strong partnerships with business, technology, and Data & AI stakeholders to accelerate solution adoption. • Communicate analytical insights, risks, recommendations, and outcomes through compelling narratives and visualizations. • Measure and improve business impact through continuous tracking of adoption, performance, and value realization. 3. What Success Looks Like • Machine learning and AI solutions deliver measurable operational, financial, and productivity improvements. • Analytical products are successfully adopted and sustained across targeted business functions. • Models achieve required standards for performance, reliability, scalability, and business relevance. • AI and analytics projects progress efficiently from concept to production deployment. • Data science initiatives demonstrate compliance with governance, security, and Responsible AI requirements. • Business stakeholders make faster and better decisions through trusted analytical insights and predictive capabilities. 4. Minimum Qualifications 5. Experience • Experience developing and deploying production-grade analytical and machine learning solutions. • Experience translating business problems into analytical frameworks, predictive models, and actionable recommendations. • Experience working with cross-functional business, technology, and data teams in complex organizational environments. • Machine Learning & Artificial Intelligence • Predictive Analytics & Statistical Modeling • Data Mining & Pattern Discovery • Feature Engineering & Model Optimization • Data Visualization & Analytical Storytelling • AI Solution Design & Industrialization Business & Delivery • Python, SQL & Advanced Analytics Tools • MLOps & Model Lifecycle Management • Data Pipeline & Analytical Environment Design • AI Product Delivery & Value Realization • Data Governance & Compliance • Performance Measurement & KPI Development People & Collaboration • Stakeholder Management • Business & Technical Translation • Cross-Functional Collaboration • Problem Solving & Critical Thinking • Continuous Improvement & Accountability.

Requirements

• Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Artificial Intelligence, or a related quantitative discipline.
• 6–8 years of experience in data science, machine learning, advanced analytics, or AI-related roles.
• 6. Skills That Matter Functional Expertise
• Communication & Presentation Skills

Skills