Lead Business Analyst

Job Title – Lead Business Analyst (Senior Manager) – Manufacturing AI

Location – Hyderabad

Experience – 12-15 Years

Employment Type – Full time

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:
• Raw material handling and preparation
• Ironmaking (Blast Furnace / DRI)
• Steelmaking (BOF / EAF / Secondary metallurgy)
• Continuous casting
• Rolling mills (Hot Rolling / Cold Rolling)
• 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:
• Blast Furnace, Reheating Furnace
• BOF/EAF converters
• Continuous casters
• Rolling mills and finishing lines
• 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)
• Strong 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:
• MES systems
• Level 2 systems
• 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)

ArcelorMittal's Equal Opportunity Statement

ArcelorMittal's equal opportunity statement is a reflection of their commitment to creating a safe and inclusive workplace where everyone feels welcomed, valued, respected, and heard. The company's journey to build a diverse and inclusive workplace is guided by their longstanding belief in "Our Strength is People®." They are focused on enhancing their Diversity and Inclusion commitment with a strong sense of purpose and resolve to evolve into a more diverse and inclusive organization. ArcelorMittal's commitment to diversity and inclusion extends to all areas of their business, including recruitment, job assignment, talent development, skills enhancement, employee retention, policies, and procedures. They strive to create an environment where everyone can bring their whole self to work, where they can excel personally and professionally.