Lead Specialist, Data Science & Analytics II

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
• Ensuring data security and compliance with relevant regulations
• Staying up to date with latest technological trends and techniques in data science

• 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