Sr Business Analyst (Pharmacovigilance & AI Systems)

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Sr Business Analyst (Pharmacovigilance & AI Systems) based in India.

We are seeking an experienced Business Analyst to help shape AI-enabled pharmacovigilance systems that support drug safety, regulatory compliance, and improved patient outcomes. In this role, you will translate complex business, clinical, technical, and regulatory needs into clear, testable requirements for software platforms used across the pharmacovigilance lifecycle. You will collaborate closely with safety operations specialists, quality teams, data privacy experts, product managers, data scientists, and engineers to deliver reliable, inspection-ready solutions. A key focus will be defining how AI and agentic systems support activities such as adverse event intake, literature screening, signal detection, and regulatory reporting while maintaining appropriate human oversight. You will also help ensure that systems meet applicable GxP, data integrity, privacy, and computerised system validation requirements. This opportunity is ideal for a detail-oriented professional who combines strong requirements engineering skills with pharmacovigilance expertise and a practical understanding of AI in regulated environments.

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Accountabilities

• Lead requirements engineering: Elicit, document, review, and maintain User Requirements Specifications (URS) and Functional Requirements Specifications (FRS) through stakeholder workshops, process walkthroughs, system demonstrations, SOP reviews, and structured discussions with subject-matter experts.

• Develop clear requirements documentation: Create and maintain process maps, data-flow diagrams, user stories, acceptance criteria, business rules, data dictionaries, interface specifications, and non-functional requirements covering performance, availability, security, auditability, and data retention.

• Maintain requirements traceability: Build and manage a Requirements Traceability Matrix linking user and functional requirements to system design, test cases, and validation evidence, ensuring requirements remain verifiable throughout delivery.

• Coordinate stakeholder reviews and change control: Work with pharmacovigilance operations, Quality Assurance, IT Security, Data Privacy, Engineering, and Product Management to validate requirements, manage approved changes, prioritize features, define release scope, and support Agile planning and backlog refinement.

• Specify literature screening and adverse event intake workflows: Define requirements for literature search strategies, database integrations, duplicate detection, AI-assisted relevance screening, Individual Case Safety Report (ICSR) identification, multi-channel intake, automated data extraction, medical coding support, narrative generation, follow-up management, and E2B(R3) submissions.

• Support signal management and regulatory reporting: Translate business needs into requirements for safety data retrieval, signal detection and assessment, prioritization, tracking, periodic safety reports, source-to-text traceability, author and reviewer workflows, version control, and submission tracking.

• Define digital and social media monitoring capabilities: Specify source configurations, monitoring rules, AI-based adverse event classification, patient information handling, validity assessment, reviewer workflows, and the conversion of relevant findings into case intake processes.

• Enable quality oversight and compliance monitoring: Define requirements for Pharmacovigilance System Master File (PSMF) support, audit-trail reporting, compliance dashboards, deviation and Corrective and Preventive Action (CAPA) tracking, training evidence retrieval, and the reproducibility of AI-assisted decisions.

• Establish requirements for AI and agentic systems: Specify each AI agent’s role, inputs, tools, permitted actions, decision authority, and human-review checkpoints. Define acceptance criteria, precision and recall targets, confidence thresholds, monitoring metrics, and escalation or fallback behaviour for low-confidence inputs and unavailable models.

• Ensure AI explainability and governance: Define requirements for decision rationale, source citations, prompt and model version logging, reproducibility, evidence retention, evaluation datasets, regression testing, model and prompt change control, periodic reviews, and restrictions on using client data for model training.

• Translate regulations into testable requirements: Address applicable pharmacovigilance guidelines, electronic records and signatures, data integrity, privacy, security, retention, validation, and quality management obligations. Support risk assessments, validation strategies, test planning, and documentation aligned with relevant regulatory and industry frameworks.

• Support audits and inspections: Serve as the requirements-side point of contact during internal audits, client assessments, and regulatory inspections, helping demonstrate that system functionality, AI behaviour, and supporting evidence meet applicable expectations.

• Contribute to testing and delivery: Support User Acceptance Testing (UAT) by preparing or reviewing test scripts, coordinating business testers, triaging defects, and verifying requirements coverage. Prepare release notes, functional documentation, training materials, and client-facing explanations of the platform’s regulatory capabilities.

Requirements

• Education: Bachelor’s degree in life sciences, pharmacy, nursing, medicine, computer science, or a related discipline.

• Relevant experience: At least 6 years of experience in pharmacovigilance or pharmacovigilance systems, including a minimum of 3 years in a Business Analyst, Systems Analyst, or product requirements role involving GxP software.

• Requirements engineering expertise: Demonstrated experience authoring URS and FRS documentation for pharmacovigilance or other regulated life-sciences systems. Experience contributing to validation documentation and maintaining end-to-end traceability is highly valuable.

• Pharmacovigilance domain knowledge: Working knowledge of at least four relevant areas, such as literature monitoring, ICSR intake and processing, signal management, periodic safety reporting, digital and social media monitoring, or quality and compliance oversight.

• Safety database experience: Hands-on familiarity with at least one commercial safety database, such as Oracle Argus, LifeSphere, Veeva Vault Safety, or an equivalent platform.

• Regulatory and quality knowledge: Solid understanding of 21 CFR Part 11, EU GMP Annex 11, data integrity principles including ALCOA+, GDPR, and applicable pharmacovigilance guidelines and ICH standards. Ability to translate regulatory obligations into specific, testable system requirements is essential.

• Validation and quality management experience: Experience working within a Quality Management System and formal Software Development Life Cycle, including change control, Computerised System Validation (CSV) or Computer Software Assurance (CSA), traceability, deviation management, and CAPA processes.

• AI and automation understanding: Practical understanding of large language models, AI agents, and common failure modes, including the need for explainability, evaluation, human oversight, and controlled operation in regulated environments. Previous involvement in an AI or automation project in pharmacovigilance is strongly preferred.

• Strong analytical writing: Ability to produce precise, unambiguous requirements that engineers can implement, testers can verify, and auditors can assess. Excellent analytical thinking, documentation, and problem-solving skills are important.

• Agile delivery and tools: Familiarity with Agile methodologies and delivery tools such as Jira, Confluence, or Azure DevOps. Comfortable collaborating across technical and non-technical teams and managing evolving priorities.

• Preferred technical and regulatory expertise: Experience with E2B(R3) implementation, MedDRA and WHODrug coding, EudraVigilance or FAERS data, and requirements for NLP, machine learning, or generative AI features such as classification, extraction, and summarization.

• Additional qualifications: Familiarity with GAMP 5 second edition, the FDA Computer Software Assurance approach, the EU AI Act, and relevant EMA or FDA guidance on AI in the medicinal-product lifecycle is advantageous. Prior participation in regulatory inspections or client audits and certifications such as CBAP, PMI-PBA, or ISPE/GAMP training are also beneficial.

• Collaboration and communication: Strong interpersonal skills and the ability to work effectively with pharmacovigilance professionals, medical reviewers, quality teams, privacy and security specialists, product leaders, and software development teams while keeping patient safety and regulatory compliance at the forefront.

Benefits

• Meaningful healthcare impact: Contribute to technology and processes that support medicine safety, improve pharmacovigilance operations, and help protect patients.

• Collaborative environment: Work with multidisciplinary teams across pharmacovigilance, quality, privacy, product management, data science, and engineering.

• AI and technology exposure: Help define the next generation of AI-enabled systems in a highly regulated life-sciences environment, with opportunities to apply emerging technologies to real operational challenges.

• Professional growth: Build expertise at the intersection of pharmacovigilance, business analysis, regulated software, and AI governance.

• Inclusive workplace culture: Join a team environment that values diverse backgrounds, mutual respect, transparent communication, and shared responsibility.

• Innovation and continuous improvement: Contribute ideas, improve business processes, and help develop reliable solutions for complex healthcare and regulatory needs.

• Compensation and other benefits: Specific salary details, healthcare coverage, leave entitlements, and additional benefits were not specified in the source description and should be confirmed during the hiring process.

\nHow Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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