Manager, Data Science & Analytics

1. Manage End to End Data Science Delivery

• Oversee the development, implementation, and maintenance of databases and data collection systems.

• Manage the lifecycle of ML/AI initiatives supporting problem framing, data exploration, feature engineering, model development, validation, and preparation for MLOps deployment.

• Ensure delivery of scalable, production ready models in alignment with enterprise data governance and AI standards.

• Conduct statistical analysis to interpret data insights and support decision making.

• Apply data mining techniques to uncover patterns, trends, and relationships in large datasets.

• Develop predictive models and machine learning algorithms to forecast future outcomes as required.

• Create clear data visualizations, dashboards, and reports to effectively communicate findings to business stakeholders.

• Collaborate with cross functional teams to understand business needs and translate them into data driven solutions.

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

• Stay updated on emerging techniques, tools, and technologies in data science and analytics.

• Promote experimentation, model versioning, automated retraining, and continuous improvement processes.

• Recommend enhancements to existing models and analytics workflows for greater efficiency and impact.

2. Data Operations

• Partner with data engineering to design, operate, and maintain reliable data pipelines and analytical models, ensuring data accuracy, timeliness, and trustworthiness.

• Monitor data quality, resolve data issues, and enforce data best practices across teams.

• Support compliance with data security standards and relevant regulations

• Supervise the development and deployment of advanced ML models, predictive analytics, GenAI, and optimization solutions, consistent with responsibilities of analytics leadership roles.

• Ensure technical rigor across the end to end ML lifecycle: scoping data engineering modeling validation MLOps monitoring.

• Oversee production reliability, model drift management, and continuous improvement.

3. Team Leadership & Capability Building

• Manage and grow a multi disciplinary team (Lead Specialists, Senior Specialists, Data Scientists). Roles under this manager are explicitly listed in the org structure.

• Build technical excellence through coaching on ML, GenAI, statistics, feature engineering, and experimentation.

• Foster a culture of innovation, peer review, and reusable modeling frameworks.

4. AI Governance, Quality & Standards

• Steward high standards in model explainability, auditability, lineage, and risk management.

• Drive compliance with enterprise data governance, PDPL, and model risk principles referenced in internal DS/AI qualification guidance.

• Ensure adherence to Responsible AI requirements applicable to DS/AI roles (skills and qualifications guidance).

• Enforce data quality standards, lineage, and reproducibility across the team s projects.

• Support alignment with enterprise governance frameworks, including PDPL compliance.

Skills:

• Machine Learning & AI Leadership

• Programming Expertise

• Generative AI Proficiency

• Strong Analytical & Statistical Skills

• Business Strategy & Product Thinking

• Ethics, Govern