Role Summary

- Responsible for end-to-end identification, structuring, and enabling execution of AI and advanced analytics use cases across steel manufacturing operations
- Acts as the bridge between plant stakeholders (operations, quality, maintenance, safety) and Data/AI engineering teams
- Translates complex steel plant problems into structured, KPI-driven AI initiatives with clear scope,

assumptions, and success criteria
- Works closely with data scientists, engineers, and vendors to ensure problem definition, data readiness,

and solution alignment
- Contributes hands-on in data analysis and validation of use cases to ensure business relevance and value realization
- Expected to work hands-on on data analysis, problem structuring, and solution validation for critical or complex use cases
- Applies strong understanding of steel manufacturing processes to ensure AI solutions are practical,

scalable, and aligned with plant realities
- Prior experience working in plant environments or direct exposure to shopfloor operations is strongly preferred to ensure practical alignment with real-world manufacturing conditions

Key Responsibilities

1. Steel Manufacturing Domain Alignment

• Engage deeply with plant operations across: o Raw material handling and preparation o Ironmaking (Blast Furnace / DRI)

o Steelmaking (BOF / EAF / Secondary metallurgy)

o Continuous casting o Rolling mills (Hot Rolling / Cold Rolling)

o Finishing and downstream processing

- Map AI use cases to specific process steps, equipment, and production KPIs
- Ensure alignment with plant constraints such as production schedules, material variability, and safety requirements
- Work closely with plant SMEs to validate feasibility and assumptions
- Leverage prior plant or shopfloor experience (where available) to contextualize use cases, validate assumptions, and ensure feasibility of solutions within operational constraints

1. Steel Process and Equipment Understanding

• Develop understanding of key equipment including: o Blast Furnace, Reheating Furnace o BOF/EAF converters o Continuous casters o Rolling mills and finishing lines o Utilities and auxiliary systems
- Interpret process parameters such as temperature, pressure, flow, chemical composition, and defect indicators
- Link process behavior with data patterns to support AI insights

1.



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
- Prioritize use cases based on feasibility, impact, and scalability

1. 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

1. 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 modeling
- Support hypothesis testing and insight generation

1. 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
- Support resolution of execution bottlenecks
- Review and validate vendor-proposed approaches, data assumptions, and outputs to ensure alignment with business objectives

1. 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

1. 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





Key AI Use Cases in Steel Manufacturing (Context for Role)

- Blast Furnace performance optimization 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 optimization across furnaces and utilities
- Yield improvement and process optimization
- Safety analytics and incident prediction

Good to Have

1. AI Solution Framing and Validation

• Collaborate with data scientists to define model objectives and solution approaches
- Ensure alignment between business outcomes and AI outputs
- Interpret model results in manufacturing context and validate effectiveness
- Define success metrics and track expected vs actual outcomes

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 and ability to correlate business problems logically with underlying process behaviour.
- Ability to communicate effectively with plant operations teams using domain-relevant language

(process, equipment, and KPI terminology)
- Hands-on experience in data analysis (SQL, or similar)
- Experience working with cross-functional teams (business, IT, data)
- Robust analytical thinking, structured problem solving, and communication skills

Good to have

- Fundamental understanding of AI/ML and analytics lifecycle

Preferred qualifications

- Experience in steel manufacturing or metals industry
- Strong exposure to plant processes and industrial data
- Experience working with:

o MES systems o Level 2 systems o 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 exposure to shopfloor operations

Time Zone – Selected candidate is required to work as per:

- India Time (IST) OR European Time (CET/GMT)

📌 Lead Business Analyst (Senior Manager ) – Manufacturing AI (Hyderabad)
🏢 Workfall
📍 Hyderabad