Principal Specialist, Data Science & Analytics

Job Purpose

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The Lead Specialist, Data Science & Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.

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The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.

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Lead Specialist, Data Science & Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow

Key Accountabilities:

1. Lead End-to-End Data Science Delivery

• Developing, implementing and maintaining databases and data collection systems

• Own the full lifecycle of ML/AI initiatives - from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.

• Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.

• Performing statistical analysis to understand and interpret data insights

• Applying data mining techniques to identify patterns, trends, and relationships in large datasets

• Building predictive models and machine learning algorithms to forecast future outcomes

• Creating clear data visualizations and reports to communicate findings to stakeholders

• Working with cross-functional teams to understand business needs and provide data-driven solutions

• Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use

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Ensuring data security and compliance with relevant regulations

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Drive experimentation, model versioning, automated retraining, and continuous improvement.

2. Translate Business Needs into AI/Analytics Solutions

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Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions

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Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.

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Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data & AI leadership and business teams.

3. Industrialize AI/ML Models (ML Ops & Architecture)

• Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.

• Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.

• Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins

• Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements

4. Responsible AI, Quality & Governance

• Ensure compliance with Maaden’s Responsible AI, data quality, and data governance frameworks.

• Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.

• Ensure transparency, explainability, and continuous model governance across production and enterprise environments

5. Stakeholder Management & Value Realization

• Communicate insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.

• Track value realization, adoption metrics, and operational impact to ensure measurable benefit.

Minimum Qualification, Experience and Core Competencies:

Minimum Qualifications:

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Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.

Minimum Experience:

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Minimum Experience:

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8 – 10 years’ experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred. Including at least 2 years leading or mentoring analytics professionals

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Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.

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Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS) – Microsoft AI Factory

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Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.

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Machine Learning & Advanced Analytics

• Hands-on experience developing and deploying machine learning models, including time-series forecasting, predictive modeling, and optimization use cases

• Strong understanding of model performance, validation, stability, and business impact

• Generative AI & AI Agents

• Practical experience with Generative AI solutions, including copilots, intelligent automation, and agent-based workflows

• Ability to embed GenAI capabilities into enterprise processes to improve decision-making and operational efficiency

Good to Have Capabilities

• Data Engineering (IT + OT)

• Experience designing and maintaining data pipelines across IT and OT environments

• Exposure to sensor data, streaming / real-time data processing, and industrial data sources

• Ability to collaborate with data engineering teams to ensure reliable, timely, and trusted data flows

• MLOps / AgentOps

• Experience in model deployment and lifecycle management, including:

• Transition from model development to production and scale

• Monitoring, retraining, versioning, and drift management

• Familiarity with automation and operationalization of ML/AI workloads

Preferred Experience & Platforms

• Cloud & Analytics Platforms

• Experience working with enterprise cloud platforms, preferably:

• Microsoft Azure Data Platform

• Databricks AI Platform

• Microsoft AI Foundry / Microsoft AI Factory

• Understanding of cloud-native architectures for scalable analytics and AI solutions

Core Competencies:

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Model Accuracy & Reliability: Performance, drift stability, and operational uptime.

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Adoption & Business Impact: Value realized, user adoption, integration success.

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Delivery Velocity: Timeliness of development cycles and deployment readiness.

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Compliance & Quality: Alignment with Responsible AI, governance, and documentation standards.