Lead Business Analyst

Job Title – Lead Business Analyst (Senior Manager) – Manufacturing AILocation – HyderabadExperience – 12-15 YearsEmployment Type – Full timeRole SummaryResponsible for end-to-end identification, structuring, and enabling execution of AI and advanced analytics use cases across steel manufacturing operationsActs as the bridge between plant stakeholders (operations, quality, maintenance, safety) and Data/AI engineering teamsTranslates complex steel plant problems into structured, KPI-driven AI initiatives with clear scope, assumptions, and success criteriaWorks closely with data scientists, engineers, and vendors to ensure problem definition, data readiness, and solution alignmentContributes hands-on in data analysis and validation of use cases to ensure business relevance and value realizationExpected to work hands-on on data analysis, problem structuring, and solution validation for critical or complex use casesApplies strong understanding of steel manufacturing processes to ensure AI solutions are practical, scalable, and aligned with plant realitiesPrior experience working in plant environments or direct exposure to shopfloor operations is strongly preferred to ensure practical alignment with real-world manufacturing conditionsKey ResponsibilitiesSteel Manufacturing Domain AlignmentEngage deeply with plant operations across:Raw material handling and preparationIronmaking (Blast Furnace / DRI)Steelmaking (BOF / EAF / Secondary metallurgy)Continuous castingRolling mills (Hot Rolling / Cold Rolling)Finishing and downstream processingMap AI use cases to specific process steps, equipment, and production KPIsEnsure alignment with plant constraints such as production schedules, material variability, and safety requirementsWork closely with plant SMEs to validate feasibility and assumptionsLeverage prior plant or shopfloor experience (where available) to contextualize use cases, validate assumptions, and ensure feasibility of solutions within operational constraintsSteel Process and Equipment UnderstandingDevelop understanding of key equipment including:Blast Furnace, Reheating FurnaceBOF/EAF convertersContinuous castersRolling mills and finishing linesUtilities and auxiliary systemsInterpret process parameters such as temperature, pressure, flow, chemical composition, and defect indicatorsLink process behavior with data patterns to support AI insightsUse Case Identification and Problem StructuringIdentify AI and analytics opportunities across steel manufacturing processesConvert plant-level operational challenges into structured problem statementsDefine KPIs such as yield, throughput, quality, energy consumption, and downtime reductionPrioritize use cases based on feasibility, impact, and scalabilityBusiness Analysis and Requirements DefinitionGather and document functional, process, and data requirementsDevelop use case charters, business requirement documents, and solution notesDefine assumptions, constraints, risks, and dependenciesAct as primary interface between plant stakeholders and AI/data teamsData Understanding and Analytical SupportPerform exploratory data analysis on plant data (process parameters, sensor data, quality data)Validate data availability, quality, and readiness for AI use casesWork with engineering teams on data pipelines, feature definition, and data modelingSupport hypothesis testing and insight generationDelivery Support and Execution GovernanceTrack execution of AI use cases and ensure alignment with defined scopeManage risks, dependencies, and change requestsCoordinate across plant teams, IT, data teams, and vendorsSupport resolution of execution bottlenecksReview and validate vendor-proposed approaches, data assumptions, and outputs to ensure alignment with business objectivesValue Realization and Impact TrackingDefine frameworks to track business value from AI initiativesMeasure impact across cost reduction, quality improvement, productivity, and efficiencySupport scaling of successful use cases across plantsStakeholder Communication and GovernancePrepare structured, executive-ready documentation for decision-makingCommunicate insights, risks, and outcomes to business and leadership stakeholdersSupport governance forums and reportingKey AI Use Cases in Steel Manufacturing (Context for Role)Blast Furnace performance optimization and permeability predictionPredictive maintenance for rotating and hydraulic equipmentContinuous caster defect prediction and breakout preventionRolling mill quality defect detection and root cause analysisEnergy optimization across furnaces and utilitiesYield improvement and process optimizationSafety analytics and incident predictionGood to HaveAI Solution Framing and ValidationCollaborate with data scientists to define model objectives and solution approachesEnsure alignment between business outcomes and AI outputsInterpret model results in manufacturing context and validate effectivenessDefine success metrics and track expected vs actual outcomesRequired qualificationsBachelor’s degree in Engineering10+ years of experience in Business Analysis, Analytics, or Digital rolesStrong experience in translating manufacturing business problems into structured analytical use casesDeep understanding of manufacturing process terminology and ability to correlate business problems logically with underlying process behaviourAbility 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 skillsGood to haveFundamental understanding of AI/ML and analytics lifecyclePreferred qualificationsExperience in steel manufacturing or metals industryStrong exposure to plant processes and industrial dataExperience working with:MES systemsLevel 2 systemsIndustrial 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 modelsPrior experience working in steel manufacturing plants or industrial environments with direct exposure to shopfloor operationsTime Zone – Selected candidate is required to work as per:India Time (IST) OR European Time (CET/GMT)ArcelorMittal's Equal Opportunity StatementArcelorMittal'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.