Process Mapping & Optimization — Lead end‑to‑end process discovery, modeling (BPMN), gap analysis, and redesign for asset lifecycle workflows (acquisition, maintenance, depreciation, disposal).
Requirements Engineering — Gather, document, and validate business, functional, and data requirements for system enhancements and new capabilities.
Asset Management Domain Analysis — Translate asset operations needs into system features, ensuring compliance with financial, regulatory, and audit standards.
Data & AI Enablement — Define data models, metadata needs, and integration points to support analytics, predictive maintenance, anomaly detection, and AI‑driven insights.
Stakeholder Management — Facilitate workshops, drive alignment, and manage expectations across business, IT, data engineering, and leadership teams.
Solution Evaluation — Assess vendor solutions, internal prototypes, and AI models for feasibility, scalability, and business value.
Documentation & Governance — Maintain BRDs, user stories, process maps, data dictionaries, and test scenarios.
Change Management — Support UAT, training, adoption, and rollout of new processes and system capabilities.
Apply Value Stream Mapping (VSM) to identify waste, bottlenecks, and cost drivers across asset lifecycle workflows, quantifying improvement opportunities.
Lead Business Process Re-engineering (BPR) initiatives to challenge and redesign legacy processes, reducing cycle times, manual handoffs, and operational risk at scale.
2–7 years of experience working as a Business Analyst, with experience in Rail Asset Management.
Strong experience with Asset Management Systems (EAM, CMMS, ERP Asset Modules).
Hands‑on expertise in BPMN, process modeling tools (Visio, Signavio, ARIS).
Solid understanding of data flows, data quality, master data, and metadata management.
Exposure to AI/ML concepts such as predictive maintenance, forecasting, anomaly detection, NLP, or automation.
Ability to write clear user stories, acceptance criteria, and process documentation.
Experience working with data engineering, BI teams, and cloud platforms (Azure/AWS/Google Cloud Platform).
Strong analytical, communication, and cross‑functional collaboration skills.
Note: Quarterly travel for PI meetings.
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