Manager, Data Science & Analytics

Role Purpose

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.

Minimum Qualifications

Minimum Experience

• 10 – 12+ years’ experience in advanced analytics, ML/AI engineering, and industrial data science.
• Including at least 4 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).
• Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.
• Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch, Azure, AWS).
• Proven record of enabling cross-functional business impact through scalable, production-grade data science solutions.

Core Competencies

• Value realization from ML/analytics initiatives (impact delivered vs. target).
• On‑time delivery of analytics roadmap.
• Adoption rate and integration of solutions into business workflows.
• Model performance stability (drift incidents, uptime).
• Team capability maturity and talent development indicators.

• Machine Learning & AI Leadership

• Programming Expertise