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
- Ensure data security and compliance with relevant regulations
- Drive experimentation, model versioning, automated retraining, and continuous improvement.
2. Translate Business Needs into AI/Analytics Solutions
- Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions
- Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
- 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 Qualifications:
- Bachelor s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.
Minimum Experience: br> br>
- Minimum Experience:
- 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
- Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
- Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS) Microsoft AI Factory
- Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.
- 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 efficiencybr> br>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 workloadsbr> br>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:
- Model Accuracy & Reliability: Performance, drift stability, and operational uptime.
- Adoption & Business Impact: Value realized, user adoption, integration success.
- Delivery Velocity: Timeliness of development cycles and deployment readiness.
- Compliance & Quality: Alignment with Responsible AI, governance, and documentation standards.
