MET scientist

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

Qualifications :

Knowledge experience & capabilities

Critical knowledge

• Deep understanding of machine learning algorithms statistical modeling and AI techniques (supervised/unsupervised learning deep learning time-series forecasting optimization)
• Knowledge of MLOps practices model lifecycle management and production deployment architectures
• Understanding of data analytics platforms (Seeq OSIsoft PI cloud analytics tools) and their integration with ML workflows
• Understanding of data engineering principles ETL pipelines data governance and data quality management
• Familiarity with cloud architectures (AWS/Azure/GCP) and distributed computing frameworks
• Proficiency in Python/R and ML frameworks (scikit-learn TensorFlow PyTorch XGBoost)
• Knowledge of version control (Git) code review practices and software engineering principles
• Understanding of manufacturing data systems and industrial IoT data streams

Critical experience

• Minimum 2 years of hands-on experience in data science machine learning and AI development
• Experience of deploying production-grade ML models in manufacturing/industrial settings
• Experience with agile delivery DevOps and CI/CD practices for analytics solutions
• Demonstrated success in delivering measurable business impact through AI/ML initiatives (ROI efficiency gains predictive accuracy quality improvements)
• Experience working with cross-functional teams (data engineers IT operations business stakeholders)

Critical technical professional and personal capabilities

• Analytical Thinking: Strong problem-solving root cause analysis and diagnostic capabilities
• Technical Communication: Translates complex ML concepts into business value and actionable insights for diverse audiences
• Self-Driven: Takes initiative works independently and drives solutions from concept to production
• Attention to Detail: Ensures model accuracy data quality and robust validation
• Continuous Learning: Stays current with latest research tools and industry best practices in AI/ML
• Curiosity & Innovation: Explores new techniques and challenges conventional approaches
• Collaboration: Works effectively with diverse technical and non-technical teams
• Adaptability: Comfortable with ambiguity changing requirements and iterative development
• Results-Oriented: Focuses on delivering value and measurable outcomes

Critical leadership capabilities

• Drives Technical Excellence: Sets high standards for code quality model performance and analytical rigor
• Fosters Innovation Culture: Encourages experimentation learning from failures and data-driven decision making
• Focuses on Customer Value: Understands operational pain points and delivers solutions that create tangible business impact
• Collaborates Across Boundaries: Bridges gaps between data science engineering IT and operations teams
• Influences Through Expertise: Drives adoption and change through technical credibility and value demonstration

Critical success factors & key challenges

• Data Quality & Availability: Navigating inconsistent data quality missing historical data and fragmented data sources across manufacturing sites and systems
• Legacy System Integration: Connecting modern AI/ML solutions with legacy manufacturing systems (historians MES SCADA) that may have limited APIs or data accessibility
• Organizational Change Resistance: Overcoming skepticism toward AI/ML predictions and building trust in model outputs among operations teams accustomed to traditional decision-making
• Balancing Innovation with Delivery: Managing the tension between exploring cutting-edge AI techniques and delivering practical production-ready solutions within business timelines
• Cross-Functional Coordination: Aligning priorities and timelines across multiple teams (data engineering IT infrastructure site operations DPI) with competing demands and resource constraints
• Scaling Across Sites: Adapting and deploying ML solutions across diverse manufacturing environments with varying processes equipment and data maturity levels

Additional Information :

• We offer a variety of financial and non-financial benefits including: XXX
• We offer a position which contributes to valuable and impactful work in a stimulating and international environment 
• A superb working environment with an open culture and diverse workforce where new ideas are always welcome
• The opportunity to work with and learn from highly qualified and experienced employees and gain scientific and technical excellence
• Learning culture (Together we Grow) and wide range of training options
• You will profit from a competitive pension fund plan attractive bonus system onsite doctor fitness room canteen and other benefits (Family friendly initiatives Child and Family allowance)
• The successful applicant must be able to demonstrate that they are eligible to live and carry out the particular role in Pune

Innovations

• Employee may as part of his/her role and maybe through multifunctional teams participate in the creation and design of innovative this context Employee may contribute to inventions designs other work product including know-how copyrights software innovations solutions and other intellectual assets.

Remote Work :

No

Employment Type :

Full-time