# Senior Business Analyst at LSEG

LSEG is hiring a Senior Business Analyst in Bengaluru, India. Level rates it Works on AI ●●●○; you can [apply on LSEG](https://jobsbylevel.com/go/1cc61948-4dee-4085-8d0f-0ea7ad54f0a0?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. IND-Bangalore-TowerE,RMZ Infin.

## Details

- Company: [LSEG](https://jobsbylevel.com/companies/lseg)
- AI level: AI Level 3 (score 65 out of 100)
- Location: IND-Bangalore-TowerE,RMZ Infin
- Posted: November 4, 2026
- Apply: https://jobsbylevel.com/go/1cc61948-4dee-4085-8d0f-0ea7ad54f0a0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-business-analyst-at-lseg-96c89f

## Prepare for this job

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## Description

Senior Business Analyst Job Description Summary The Business Analyst will work closely with Data Science, AI Engineering, Product Owners, and business teams to support the development and delivery of AI/ML and Agentic AI solutions. The role involves understanding business requirements, translating them into detailed user stories, and collaborating with Data Scientists and development teams to ensure solutions meet business objectives. The Business Analyst will support AI use case identification, data analysis, requirements documentation, model evaluation, and Agile delivery activities. The role requires strong analytical capabilities, technical understanding, and an interest in emerging AI technologies, particularly Generative AI and Agentic AI. The role also supports AI-specific non-functional requirements, Responsible AI controls, risk identification, and governance documentation. Key Responsibilities 1. Business Requirements and Analysis Engage with business stakeholders to gather, analyse, and document requirements for AI/ML and Agentic AI solutions. Translate business needs into well-defined technical requirements for Data Science team, multiple Epics, user stories, and acceptance criteria. Work with Data Scientists and AI Engineers to understand business rules, data requirements, expected outputs, and solution workflows. Define business scenarios, exception handling, and success criteria for AI-driven solutions. Ensure requirements are clearly documented, testable, and aligned with business objectives. 2. Data Science and Agentic AI Collaboration Work closely with Data Science teams to support the development and implementation of AI/ML models and Agentic AI workflows. Understand AI solution workflows, including data extraction, processing, validation, and decision-making. Support the definition of requirements for AI agents, including agent responsibilities, workflow orchestration, tool interactions, and human-in-the-loop processes. Collaborate with technical teams to identify data dependencies, integration requirements, and potential solution limitations. Assist in identifying opportunities for process automation and efficiency improvements using AI technologies. 3. Data Analysis and AI Evaluation Support data analysis, data preparation, and validation activities required for AI/ML solutions. Coordinate with business teams to define sampling methodologies and prepare representative Golden Datasets for model training and evaluation. Work with Data Scientists to define evaluation criteria and business success metrics, including precision, recall, accuracy, and automation rates, where applicable. Support model output validation, error analysis, and identification of improvement opportunities. Analyse discrepancies between expected and actual AI outputs and coordinate corrective actions with relevant teams. Support business acceptance testing and ensure AI solutions satisfy agreed success criteria. 4. Responsible AI, Risk & Governance Ensure requirements consider data privacy, security, bias, fairness, explainability, transparency, and appropriate human oversight. Work with Data Science, Security, Risk, and Compliance teams to identify, document, and track AI-related risks and mitigating controls. Support AI governance and model approval documentation, risk assessments, and auditability, including decision traceability, access controls, logging, and monitoring. Capture risks including hallucinations, data leakage, prompt injection, model drift, and unintended agent actions; document guardrails, human review, fallback, and escalation requirements in acceptance criteria. 5. Technical Understanding, API Testing & NFRs Analyse existing systems, data structures, and workflows to support AI solution integration. Understand and document data schemas, data mappings, JSON, XML/XSD, and API integration requirements. Use Postman or similar API testing tools to inspect and validate API requests, responses, and integrations alongside

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