Manager, Data Science & Analytics
Skills
About This Role
Overview
- 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.
• Machine Learning & AI Leadership
- Programming Expertise
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