Senior Data Scientist

• 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
• 1. 35-40 yrs
• 2. Flexible for exceptional candidates with high-impact leadership experience
• Qualifications
• Essential from reputed institutions
• 1. Bachelors degree in Engineering (Mechanical, Chemical, Industrial, or IT preferred)
• 2. Masters in Data Science / Statistics / Computer Science / Operations Research /
• Analytics
• Desirable
• 3. MBA (optional but preferred for business alignment)
• 4. PhD in AI/ML, Data Science, or Applied Statistics
• 5. Certifications in Cloud (Azure, AWS, GCP), Data Engineering, or Six Sigma
• Experience
• Essential
• 1. 10+ years of total experience
• 2. 5+ years in Data Science / Advanced Analytics / AI
• 3. 2+ years in leadership roles to manage large teams
• 4. Proven experience delivering enterprise-scale AI & AA programs
• Desirable
• 5. Experience in manufacturing / industrial / chemical / process industries
• 6. Exposure to Industry 4.0 / IoT / digital manufacturing transformation
• 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 Desirable
• 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 frameworks
• Types of Companies / Industry Background
• Preferred
• 1. Manufacturing organizations (Chemical, Petrochemical, Engineering, Automotive,
• FMCG)
• 2. Companies undergoing digital transformation or Industry 4.0 initiatives
• Desirable Exposure
• 1. Global firms with advanced analytics maturity
• 2. Consulting firms (Analytics/Digital Transformation practices)
• 3. Technology companies delivering AI/ML solutions
• If you are interested kindly send your updated resume to [HIDDEN TEXT]