Lead Business Analyst (Senior Manager ) Manufacturing AI

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

2. 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

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

• Prioritize 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 modeling

• 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

• Support resolution of execution bottlenecks

• Review and validate vendor-proposed approaches, data assumptions, and outputs to ensure alignment

with business objectives

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

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:

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)