Role purpose
Reporting to the MET data analytic lead, the role is to develop AI and data science product to answer to analytical requirement for engineering & manufacturing organization.
This role drives innovation and product development in Pune and fully integrates with existing platforms to drive synergies with P&S and DPI teams. She/He ensures quality delivery in time at full on analytical & AI products in line with customer needs. This role is key in delivering Artificial Intelligence & analytics strategy for manufacturing and engineering working in partnership with DPI, sites and engineering teams.
Accountabilities
1. AI/ML Product Development & Technical Innovation
- Design, develop, and deploy machine learning models (predictive, prescriptive, diagnostic) for manufacturing and engineering use cases
- Implement MLOps practices including model versioning, monitoring, and automated retraining
- Build scalable algorithms and integrate with analytical platforms (Seeq, cloud infrastructure)
- Scout and evaluate emerging AI/ML technologies, frameworks, and methodologies (deep learning, NLP, computer vision, time-series forecasting)
- Prototype and pilot cutting-edge solutions; conduct POCs to assess technical feasibility and ROI
2. Analytics Delivery & Platform Integration
- Translate business requirements into technical specifications, data models, and analytical architectures
- Develop analytics backlog with clear technical milestones and delivery timelines
- Integrate AI/ML solutions with DPI infrastructure, manufacturing systems, and digital platforms
- Support global Seeq deployment through technical implementation and optimization
- Build dashboards, APIs, and visualization tools for model outputs and insights
- Collaborate with data engineers on ETL pipelines, data quality, and ML infrastructure
- Ensure code quality, documentation, and adherence to software engineering best practices
3. Technical Consultation & Stakeholder Engagement
- Act as technical consultant on AI/ML methodologies, algorithm selection, and solution design
- Interface with Data Science Teams, AI/ML Engineering Teams, DPI Platform Teams, and Manufacturing/Engineering stakeholders
- Communicate technical concepts, model performance metrics, and analytical results to diverse audiences
- Drive analytics adoption through technical workshops, enablement sessions, and knowledge sharing