Senior Data Scientist

Chemplast Sanmar; Chennai District; On-site; INR 2,000,000 - 3,600,000; Full time

Job summary

Sanmar Group in Chennai invites applications for a Senior Data Scientist to lead enterprise-wide data science and AI initiatives, bridging manufacturing operations with analytics to drive cost optimization, productivity gains, and digital transformation.

The role anchors Industry 4.0, builds a capable team, defines data governance, and champions MLOps while delivering measurable business outcomes across functions such as supply chain, quality, and commercial.

Qualifications

• 10+ years total professional experience.
• 5+ years in Data Science / Advanced Analytics / AI.
• Leadership experience managing large teams.
• Experience delivering enterprise-scale data solutions.

Responsibilities

• Define enterprise data science strategy aligned to business goals.
• Drive analytics adoption across manufacturing, supply chain, quality, and commercial.
• Oversee end-to-end ML/AI model lifecycle and deployment.
• Build and scale a high-performing data science team.

Skills

Python

R

SQL

Machine Learning

Leadership

Education

Bachelor's in Engineering

Master's in Data Science/Statistics/CS/OR/Analytics

Tools

TensorFlow

Spark

Power BI

Job description

Dear Candidate,

Greetings from Sanmar Group!

We have an opportunity for Senior Data Scientist role for Corporate Office, Chennai.

Looking for candidates with 10+ years of total experience and minimum of 5+years of experience in Data Science / Advanced Analytics / AI

Details below,

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 decisionmaking.

• 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

The role encompasses three core mandates

1. Strategic Leadership

• a. Define data science roadmap aligned with business strategy
• b. Identify high-impact use cases (predictive maintenance, quality analytics, demand forecasting)

2. Operational Execution

• a. Deliver AI/ML solutions embedded in manufacturing processes & other business functions
• b. Oversee model lifecycle (development, deployment, monitoring)

3. Organizational Capability Building

• a. Build and scale a high-performing team
• b. Institutionalize data governance, platforms, and AI culture

Key Responsibilities

1. Strategy & Business Alignment

• a. Define and execute enterprise data science strategy aligned to business goals
• b. 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
• c. Act as a strategic advisor to leadership on AI-driven transformation

2. Manufacturing & Operational Analytics

• a. Drive implementation of key manufacturing analytics use cases:
  • i. Predictive maintenance
  • ii. Process optimization
  • iii. Defect detection (vision analytics)
  • iv. Energy and resource optimization
• b. Integrate OT (shop-floor sensors) with IT systems for real-time insights
• c. Enable decision-making via dashboards, predictive and prescriptive analytics

3. Delivery & Execution Excellence

• a. Oversee end-to-end lifecycle of ML/AI models
• b. Ensure deployment of scalable, production-grade solutions
• c. Drive adoption of MLOps, data engineering, and modern data architecture
• d. Ensure business value realization and measurable outcomes

4. Team & Capability Building

• a. Build and lead a multidisciplinary team (budding data scientists, engineers, analysts)
• b. Drive talent development, succession planning, and performance management
• c. Establish data science governance, standards, and best practices

5. Data Governance & Platforms

• a. Establish data governance, quality, and security frameworks
• b. Partner with IT to build scalable data platforms (cloud, IoT, data lake)
• c. Ensure compliance with data privacy and regulatory standards

6. Stakeholder Management

• a. Collaborate with cross-functional leaders (Manufacturing, Supply Chain, Sales, Corporate functions)
• b. Work with service partners in delivering solutions
• c. Translate business problems into AI & AA solutions
• d. Communicate insights effectively to non-technical stakeholders

7. Innovation & Future Readiness

• a. Track and adopt emerging technologies (AI, GenAI, Agentic AI, IoT, Digital Twins)
• b. Drive innovation pipeline and experimentation
• c. Promote a data-driven culture across the enterprise

Age Group

• 35-40 yrs
• Flexible for exceptional candidates with high-impact leadership experience

Qualifications

Essential from reputed institutions

• Bachelor’s degree in Engineering (Mechanical, Chemical, Industrial, or IT preferred)
• Master’s in Data Science / Statistics / Computer Science / Operations Research / Analytics

Desirable

• MBA (optional but preferred for business alignment)
• PhD in AI/ML, Data Science, or Applied Statistics
• Certifications in Cloud (Azure, AWS, GCP), Data Engineering, or Six Sigma

Experience

Essential

• 10+ years of total experience
• 5+ years in Data Science / Advanced Analytics / AI
• 2+ years in leadership roles to manage large teams
• Proven experience delivering enterprise-scale AI & AA programs

Desirable

• Experience in manufacturing / industrial / chemical / process industries
• Exposure to Industry 4.0 / IoT / digital manufacturing transformation

Technical

• Machine Learning, Deep Learning, Statistical Modelling, Gen AI & Agentic AI
• Python / R / SQL, Data Engineering fundamentals
• MLOps, model deployment, scalable architecture
• Data visualization tools (Power BI, Qlik, Tableau Desirable
• IoT / sensor data analytics & Digital twin technologies

Business & Domain

• Strong understanding of manufacturing value chain
• Proven ability to translate data insights into business outcomes
• KPI design and performance measurement frameworks

Types of Companies / Industry Background

Preferred

• Manufacturing organizations (Chemical, Petrochemical, Engineering, Automotive, FMCG)
• Companies undergoing digital transformation or Industry 4.0 initiatives

Desirable Exposure

• Global firms with advanced analytics maturity
• Consulting firms (Analytics/Digital Transformation practices)
• Technology companies delivering AI/ML solutions