Principal Specialist, Data Science & Analytics
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Key skills for this role
About the Role
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 proble.
Key Skills for This Role
Full Job Posting
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 And 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.
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