Data Scientist

• WET-Water & Effluent Treatment IC
• Kancheepuram

Knowledge & Posting Location

Minimum Qualification

• Develop AI/ML models for water quality prediction, anomaly detection, failure diagnostics, and process optimization.
• Clean, structure, analyze, and visualize plant, laboratory, and field datasets for actionable insights.
• Implement regression, classification, clustering, and time-series forecasting techniques for operational improvement.

• Work under the direction of Digital Lead on business-critical, time-bound digital transformation projects.
• Develop scalable digital architectures enabling total plant automation, performance optimization, and significant shifts in asset entitlement.
• Manage the design and development of the central command centre and build sustainable data pipelines for high-volume data streaming from multiple projects.
• Lead development, piloting, and scaling of digital twin frameworks; extend capabilities to operator advisory and supervisory decision systems.

• Integrate AI/ML models with IoT gateways, SCADA, PLC/DCS systems, and cloud/edge platforms.
• Ensure compliance with IT security protocols and manage customer data with the highest degree of confidentiality.

• Collaborate with process, mechanical, chemical, and digital teams to align digital insights with physical system behaviour.
• Support dashboard development for pilot plants and enterprise-wide monitoring.
• Contribute to product catalogues, training modules, and internal knowledge-hub dossiers.
• Publish and present outcomes in data science, digital water, and sustainability forums.

• Education: PhD/M.Tech/Master’s in Engineering or Science with formal training in Data Science, Computer Science, IT, or Process Engineering.
• Experience: Minimum 7 years of experience after formal education, with at least 3 years applying data analytics in water/process industries.
• Technical Expertise: Hands-on experience with cloud, fog, and edge platforms.
• Strong knowledge of data structures, data management, data modelling.
• Experience with Kubernetes, Docker, containerized deployments.
• Exposure to SCADA, PLC, DCS, IoT architectures is highly valued.
• Ability to build digital twins and AI/ML-based models for real-time systems.
• Proficiency in Python, R, TensorFlow, PyTorch or similar ML frameworks.
• Experience with visualization tools like Power BI, Tableau, and handling time-series data.
• Publications: Preferred – 2-3 peer-reviewed publications or equivalent technical contributions in data science, digital water, or automation systems.
• Strong communication, documentation, and project management skills.