Lead Data Scientist (AI & Advanced Analytics)

We are looking for an experienced Data Science Lead to build and scale enterprise-wide data science, AI, and advanced analytics capabilities. This role will lead the Data Science charter within the AI and Analytics Centre of Excellence (CoE) and work closely with manufacturing, supply chain, quality, sales, and corporate functions to identify and deliver high-impact AI initiatives. The ideal candidate combines strong data science and AI expertise with business leadership and manufacturing domain understanding and has demonstrated experience taking AI/ML solutions from problem identification to production deployment and measurable business impact. Key outcomes will include cost optimization, productivity improvement, predictive maintenance, yield improvement, supply-chain efficiency, working-capital optimization, and digital transformation.

The core responsibilities for the job include the following:

Data Science and AI Strategy:

• Define and execute the enterprise data science and AI roadmap aligned with business priorities.
• Identify and prioritize high-value AI and advanced analytics opportunities across manufacturing operations, supply chain and logistics, quality, sales and commercial functions, Other corporate functions.
• Act as a strategic advisor to senior leadership on AI-led transformation and adoption.

Manufacturing and Operational Analytics:

• Lead implementation of advanced analytics use cases, including: Predictive maintenance and downtime reduction, process and production optimization, OEE and yield improvement, Defect detection and computer vision, demand forecasting, inventory and logistics optimization, energy and resource optimization, and predictive and prescriptive analytics.
• Drive integration of shop-floor/OT sensor data with enterprise IT systems to enable real-time decision-making.

AI/ML Delivery and MLOps:

• Own the end-to-end lifecycle of AI/ML solutions from problem formulation and modelling to deployment, monitoring, and continuous improvement.
• Build scalable, production-grade AI solutions.
• Establish strong MLOps, model governance, and deployment practices.
• Partner with data engineering and IT teams on modern data architectures and platforms.
• Ensure AI initiatives deliver measurable business outcomes and ROI.

Team and Capability Building:

• Build and lead a multidisciplinary team of data scientists, data engineers, and analysts.
• Mentor and develop technical talent while establishing strong performance standards.
• Define data science governance, methodologies, reusable frameworks, and best practices.
• Build an enterprise-wide culture of data-driven decision-making.

Data Platforms and Governance:

• Partner with IT and technology teams to build scalable cloud, IoT, and data-platform capabilities.
• Establish frameworks for data quality, governance, security, and responsible AI.
• Ensure compliance with applicable data privacy and regulatory requirements.

Stakeholder and Business Management:

• Partner with leaders across manufacturing, supply chain, quality, sales, and corporate functions.
• Translate complex business problems into scalable AI and analytics solutions.
• Communicate analytical insights and recommendations effectively to senior and non-technical stakeholders.
• Manage technology and service partners involved in solution delivery.

Innovation and Future Technologies:

• Evaluate and drive adoption of emerging technologies including: Generative AI, Agentic AI, IoT and sensor analytics, Digital Twins, Computer Vision, Advanced Machine Learning.
• Build a structured experimentation and innovation pipeline that can translate emerging technologies into business use cases.

Requirements:

• Preferred qualifications from reputed institutions:
• Bachelor's degree in engineering, computer science, mathematics, statistics, economics, or related disciplines.
• Master's degree in data science, computer science, statistics, operations research, analytics, or related areas.
• Ideal Candidate: The ideal candidate is a business-oriented data science leader who can combine deep technical understanding with strong execution and stakeholder management.

Must Have:

• 6+ years of overall professional experience.
• 5+ years of experience in data science, AI, or advanced analytics.
• At least 2+ years of experience leading data science/analytics teams.
• Proven experience delivering enterprise-scale AI/ML or advanced analytics programs.
• Strong experience taking ML models from experimentation to production.
• Demonstrated ability to translate AI initiatives into measurable business outcomes.

Preferred:

• Experience in manufacturing, chemical, petrochemical, engineering, automotive, FMCG, or other industrial/process industries.
• Exposure to Industry 4.0 smart manufacturing, IoT, or digital manufacturing transformation.
• Experience working with global organizations with mature data science/analytics practices.
• Experience in analytics, AI, or digital transformation consulting can also be relevant.

Technical Skills:

• Strong hands-on understanding of machine learning, deep learning, statistical modeling, generative AI, agentic AI, Python / R, SQL, data engineering fundamentals, MLOps and model deployment, scalable ML architecture, cloud platforms AWS / Azure / GCP, and data visualization (Power BI / Tableau / Qlik).
• Good to Have: Computer vision, IoT/sensor data analytics, digital twin technologies, data lakes, and modern data platforms.

Business and Domain Skills:

• Strong understanding of the manufacturing value chain.
• Ability to translate data and AI opportunities into measurable business outcomes.
• Strong KPI design and performance measurement capabilities.
• Excellent stakeholder management and communication skills.
• Ability to operate effectively with both business and technology leadership.