Role : Data Science Lead

Industry : Any Manufacturing

Job Location : Chennai

Job Profile :

The Lead of Data Science will be an operational leader responsible for building and scaling enterprise-wide data science and AI capabilities. The role will act as a bridge between manufacturing operations and advanced analytics, driving measurable business outcomes such as cost optimization, productivity improvement, predictive maintenance, supply chain efficiency, and digital transformation.

This position will lead the Data Science part of AI & Analytics Centre of Excellence (CoE) and anchor transformation toward Industry 4.0, smart manufacturing, and data-driven decision making.

- Own enterprise data science and AI strategy

- Drive adoption of analytics across manufacturing, supply chain, quality, and commercial functions

- Deliver quantifiable ROI (cost savings, uptime, yield improvement, working capital optimization)

- Manufacturing analytics plays a critical role in optimizing production, reducing downtime, improving quality, and enabling data-driven decisions across operations.

Job Content :

The role encompasses three core mandates.

1. Strategic Leadership :

- 1a. Define data science roadmap aligned with business strategy

- 1b. Identify high-impact use cases (predictive maintenance, quality analytics, demand forecasting)

2. Operational Execution :

- 2a. Deliver AI/ML solutions embedded in manufacturing processes & other business functions

- 2b. Oversee model lifecycle (development, deployment, monitoring)

3. Organizational Capability Building :

- 3a. Build and scale a high-performing team

- 3b. Institutionalize data governance, platforms, and AI culture

Key Responsibilities :

1. Strategy & Business Alignment :

- 1a. Define and execute enterprise data science strategy aligned to business goals

- 1b. Identify high-value AI & AA opportunities across i. Manufacturing operations (OEE, yield, downtime) ii. Supply chain (inventory, logistics optimization) iii. Quality control (defect detection) iv. Sales & other corporate functions

- 1c. Act as a strategic advisor to leadership on AI-driven transformation

2. Manufacturing & Operational Analytics :

- 2a. Drive implementation of key manufacturing analytics use cases : i. Predictive maintenance ii. Process optimization iii. Defect detection (vision analytics) iv. Energy and resource optimization

- 2b. Integrate OT (shop-floor sensors) with IT systems for real-time insights

- 2c. Enable decision-making via dashboards, predictive and prescriptive analytics

3. Delivery & Execution Excellence :

- 3a. Oversee end-to-end lifecycle of ML/AI models

- 3b. Ensure deployment of scalable, production-grade solutions

- 3c. Drive adoption of MLOps, data engineering, and modern data architecture

- 3d. Ensure business value realization and measurable outcomes

4. Team & Capability Building :

- 4a. Build and lead a multidisciplinary team (budding data scientists, engineers, analysts)

- 4b. Drive talent development, succession planning, and performance management

- 4c. Establish data science governance, standards, and best practices

5. Data Governance & Platforms :

- 5a. Establish data governance, quality, and security frameworks

- 5b. Partner with IT to build scalable data platforms (cloud, IoT, data lake)

- 5c. Ensure compliance with data privacy and regulatory standards

6. Stakeholder Management :

- 6a. Collaborate with cross-functional leaders (Manufacturing, Supply Chain, Sales, Corporate functions)

- 6b. Work with service partners in delivering solutions

- 6c. Translate business problems into AI & AA solutions

- 6d. Communicate insights effectively to non-technical stakeholders

7. Innovation & Future Readiness :

- 7a. Track and adopt emerging technologies (AI, GenAI, Agentic AI, IoT, Digital Twins)

- 7b. Drive innovation pipeline and experimentation

- 7c. Promote a data-driven culture across the enterprise

Skills Essential :

Technical :

- 1. Machine Learning, Deep Learning, Statistical Modelling, Gen AI & Agentic AI

- 2. Python / R / SQL, Data Engineering fundamentals

- 3. MLOps, model deployment, scalable architecture

- 4. Data visualization tools (Power BI, Qlik, Tableau)

- 5. IoT / sensor data analytics & Digital twin technologies

Business & Domain :

- 1. Strong understanding of manufacturing value chain

- 2. Proven ability to translate data insights into business outcomes

- 3. KPI design and performance measurement framework