Principal Specialist, Data Science & Analytics

Overview

The Principal Specialist in Data Science & Analytics serves as the technical leader responsible for spearheading the creation, implementation, and scaling of Machine Learning, AI, and data analytics solutions throughout the organization. This role focuses on delivering analytics products that are validated, governed, and operational at scale to generate tangible benefits across mining, processing, operations, and enterprise sectors.

Key Responsibilities

• Lead comprehensive Data Science projects from conceptualization through to production deployment and ongoing maintenance.
• Design, implement, and maintain robust databases and data collection systems.
• Manage the entire machine learning lifecycle including problem definition, data exploration, feature creation, model development, validation, and transition to operations.
• Develop scalable, production-quality models that comply with organizational data governance and AI principles.
• Conduct rigorous statistical evaluations to extract meaningful insights from data.
• Utilize data mining techniques to discover patterns and relationships within large datasets.
• Create predictive models and machine learning algorithms aimed at forecasting outcomes.
• Generate clear data visualizations and reports to effectively communicate conclusions to stakeholders.
• Partner with cross-functional teams to align analytics solutions with business requirements.
• Collaborate with data engineering to establish dependable data pipelines ensuring timely and accurate data delivery.
• Ensure data security practices align with pertinent regulatory standards.
• Champion experimentation including model versioning, automated retraining, and continuous optimization.
• Translate business challenges into actionable AI and analytics solutions by engaging domain stakeholders and defining measurable success criteria.
• Collaborate with data and AI leadership to develop solution roadmaps and KPIs.
• Coordinate with data engineering, platform, and cloud teams to integrate models into enterprise systems and operational technology layers.
• Define standards for production deployment, testing, monitoring, and governance of AI models including addressing model drift.
• Incorporate machine learning, optimization techniques, and computer vision to enhance performance, reliability, and sustainability.
• Adhere to responsible AI guidelines, quality assurance, and governance policies including documentation and auditability.
• Effectively present insights, risks, and recommendations to decision-makers with compelling narratives and visual aids.
• Monitor value realization, adoption rates, and operational impacts to confirm measurable improvements.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related technical discipline.
• Minimum of 8 to 10 years’ experience in Data Science or Advanced Analytics preferably within industrial, mining, or heavy-asset sectors, including at least 2 years in a leadership or mentorship capacity.
• Demonstrated ability to convert business problems into analytical methodologies through hypothesis definition, analysis planning, and result synthesis.
• Expertise with modern Machine Learning frameworks such as TensorFlow and PyTorch, and cloud platforms like Microsoft Azure and AWS.
• Strong capabilities in analytics technologies including data modeling, SQL, and collaboration with engineering teams.
• Hands-on experience in developing and deploying ML models covering time-series forecasting, predictive analytics, and optimization.
• Proficient in evaluating model performance, ensuring validation, stability, and understanding business impacts.
• Experience with Generative AI applications, including intelligent automation and agent-based workflows with the ability to integrate these solutions within enterprise processes.
• Skills in data engineering covering both IT and OT environments, with experience handling sensor data, real-time streaming, and industrial data sources.
• Knowledge in MLOps/AgentOps encompassing model deployment, lifecycle management, monitoring, retraining, and drift management.
• Familiarity with cloud and analytics platforms such as Microsoft Azure Data Platform, Databricks AI Platform, and Microsoft AI Foundry.
• Core strengths include maintaining model accuracy, driving adoption, adhering to compliance standards, and ensuring timely delivery.