About PlugScale Innovation Labs - Hyderabad:

PlugScale Innovation Labs is a specialist talent and digital consulting partner focused on enabling enterprise transformation. We work with leading global organisations across manufacturing, energy, and industrials connecting top-tier technology talent with high-impact digital programmes. All client identities are kept strictly confidential during the initial engagement process.

Role Summary:

The Lead Business Analyst - Manufacturing AI will serve as the critical bridge between plant stakeholders and Data/AI engineering teams.

The incumbent will be responsible for:

- End-to-end identification, structuring, and enabling execution of AI and advanced analytics use cases across steel manufacturing operations.

- Translating complex plant-level operational challenges into structured, KPI-driven AI initiatives with clearly defined scope, assumptions, and success criteria.

- Working closely with data scientists, engineers, and vendors to ensure problem definition, data readiness, and solution alignment.

- Performing hands-on data analysis and validation of use cases to ensure business relevance and value realization.

- Applying strong understanding of steel manufacturing processes to ensure AI solutions are practical, scalable, and aligned with plant realities.

- Prior experience in plant environments or direct shopfloor exposure is strongly preferred.

Key Responsibilities:

1. Steel Manufacturing Domain Alignment:

- Engage deeply with plant operations across:

1. Raw material handling and preparation.

2. Ironmaking (Blast Furnace / DRI).

3. Steelmaking (BOF / EAF / Secondary metallurgy).

4. Continuous casting.

5. Rolling mills (Hot Rolling / Cold Rolling).

6. Finishing and downstream processing.

- Map AI use cases to specific process steps, equipment, and production KPIs.

- Ensure alignment with plant constraints - production schedules, material variability, and safety requirements.

- Work closely with plant SMEs to validate feasibility and assumptions.

- Leverage prior shopfloor experience to contextualise use cases and validate operational feasibility.

2. Steel Process and Equipment Understanding:

- Develop strong understanding of key equipment including Blast Furnace, Reheating Furnace, BOF/EAF converters, continuous casters, rolling mills, and utilities.

- Interpret process parameters such as temperature, pressure, flow, chemical composition, and defect indicators.

- Link process behaviour with data patterns to support AI insights.

3. Use Case Identification and Problem Structuring:

- Identify AI and analytics opportunities across steel manufacturing processes.

- Convert plant-level operational challenges into structured problem statements.

- Define KPIs such as yield, throughput, quality, energy consumption, and downtime reduction.

- Prioritise use cases based on feasibility, impact, and scalability.

4. Business Analysis and Requirements Definition:

- Gather and document functional, process, and data requirements.

- Develop use case charters, business requirement documents, and solution notes.

- Define assumptions, constraints, risks, and dependencies.

- Act as primary interface between plant stakeholders and AI/data teams.

5. Data Understanding and Analytical Support:

- Perform exploratory data analysis on plant data (process parameters, sensor data, quality data).

- Validate data availability, quality, and readiness for AI use cases.

- Work with engineering teams on data pipelines, feature definition, and data modelling.

- Support hypothesis testing and insight generation.

6. Delivery Support and Execution Governance:

- Track execution of AI use cases and ensure alignment with defined scope.

- Manage risks, dependencies, and change requests.

- Coordinate across plant teams, IT, data teams, and vendors.

- Review and validate vendor-proposed approaches, data assumptions, and outputs for business alignment.

7. Value Realization and Impact Tracking:

- Define frameworks to track business value from AI initiatives.

- Measure impact across cost reduction, quality improvement, productivity, and efficiency.

- Support scaling of successful use cases across plants.

8. Stakeholder Communication and Governance:

- Prepare structured, executive-ready documentation for decision-making.

- Communicate insights, risks, and outcomes to business and leadership stakeholders.

- Support governance forums and reporting cadences.

Key AI Use Cases in Scope (Context for Role):

- Blast Furnace performance optimisation and permeability prediction.

- Predictive maintenance for rotating and hydraulic equipment.

- Continuous caster defect prediction and breakout prevention.

- Rolling mill quality defect detection and root cause analysis.

- Energy optimisation across furnaces and utilities.

- Yield improvement and process optimisation.

- Safety analytics and incident prediction.

Required Qualifications:

- Bachelor's degree in Engineering.

- 10+ years of experience in Business Analysis, Analytics, or Digital roles.

- Strong experience in translating manufacturing business problems into structured analytical use cases.

- Deep understanding of manufacturing process terminology with ability to correlate business problems with underlying process behaviour.

- Ability to communicate effectively with plant operations teams using domain-relevant language (process, equipment, KPI terminology).

- Hands-on experience in data analysis using SQL or similar tools.

- Experience working with cross-functional teams (business, IT, data).

- Strong analytical thinking, structured problem solving, and communication skills.

Good to Have:

- Fundamental understanding of AI/ML and analytics lifecycle.

- Ability to collaborate with data scientists to define model objectives, interpret results in manufacturing context, and validate effectiveness.

- Experience defining success metrics and tracking expected vs. actual outcomes.

Preferred Qualifications:

- Experience in steel manufacturing or metals industry.

- Strong exposure to plant processes and industrial data.

- Experience working with MES systems, Level 2 systems, and industrial data historians (e.g., PI System).

- Understanding of manufacturing KPIs: yield, OEE, throughput, energy.

- Experience with AI/analytics platforms and cloud environments (Azure preferred).

- Exposure to vendor-led or consulting-led delivery models.

- Prior experience working in steel manufacturing plants or industrial environments with direct shopfloor exposure.