Roles Responsibilities

Analytics Delivery AI Enablement

- Hands-on build: Develop, prototype, and code analytical models, datasets, and automation workflows translating business questions into analytical approaches, executing analyses, and synthesizing findings into actionable recommendations.

- Platform contribution: Contribute reusable components (dashboards, always-on insights, scenario/measurement pipelines) to team self-service analytics hubs such as the Agentic MMx / Always-On Insights (AOI) platform, maximizing reuse and reducing manual effort.

Agile Delivery Project Management

- Sprint-based delivery: Work within an agile, sprint-based development cycle participating in sprint planning, backlog refinement, daily stand-ups, reviews, and retrospectives to deliver analytical and AI product features iteratively and predictably.

- Story ownership: Break down requirements into well-defined user stories, tasks, and acceptance criteria; provide effort estimates and track progress using collaboration and backlog tools (e. g. , Jira, Azure DevOps).

- Delivery coordination: Manage the end-to-end delivery of assigned workstreams tracking timelines, dependencies, risks, and blockers, and proactively communicating status to the Manager/Team Lead and stakeholders.

- Release readiness: Support release planning and deployment activities, ensuring features are demo-ready, documented, and aligned to the definition of done.

Quality Assurance, Testing UAT

- Quality ownership: Take strong ownership of quality across the analytics and AI product lifecycle embedding validation, peer code reviews, and best-practice standards into everyday delivery.

- Testing: Design and execute test plans, test cases, and validation checks (data quality, logic, model output, and reconciliation), including unit, integration, and regression testing of analytical assets and pipelines.

- UAT: Plan, coordinate, and support User Acceptance Testing with business stakeholders preparing UAT scripts and test data, triaging and resolving defects, capturing sign-offs, and ensuring solutions meet business requirements before go-live.

- Documentation traceability: Maintain clear documentation, defect logs, and traceability from requirements through testing to release, ensuring reproducibility and auditability





Agentic AI Capability Development

- Support the design, development, and testing of autonomous and semi-autonomous analytics agents using multi-agent frameworks, helping progress from descriptive analytics to causal analysis, root-cause insights, and predictive recommendations.

- Contribute to the AI product lifecycle proof-of-concept, pilot, and rollout while following governance standards for safety, security, ethics, and privacy.

- Apply and help operationalize LLMs for commercial use cases such as knowledge retrieval, summarization, generative analytics, and automation of insight generation.

Stakeholder Partnership Strategic Support

- Partner closely with US Commercial stakeholders, Global Analytics, OCx, Marketing, and BIT to understand business needs and contribute to solution design.

- Act as a trusted analytical partner clearly explaining insights, assumptions, and limitations, and supporting decision-making discussions.

- Support prioritization of business requests by providing effort estimates, impact assessments, and analytical recommendations.

- Contribute to stakeholder presentations, readouts, and working sessions with clear, structured storytelling.

Technical Execution, Governance Data Stewardship

- Build and maintain analytical assets including datasets, models, dashboards, and automation workflows.

- Work closely with BIT and data engineering teams to troubleshoot data issues and ensure reliable, timely, and scalable data availability.

- Ensure analytical outputs are reproducible, well-documented, explainable, and aligned with data/AI governance and compliance standards applying privacy-by-design and human-in-the-loop practices where required.

Required Qualifications

Education Experience

- BA/BS required; advanced degree preferred, especially in life sciences, computer science, mathematics, statistics, data science, or engineering.





- 3+ years of professional experience in advanced analytics, decision science, or AI-driven roles.

- Proven experience delivering end-to-end analytics projects, from problem framing to insight delivery.

- Demonstrated ability to partner with business stakeholders and support data-driven decision-making.

- Experience in pharmaceutical, biotech, or healthcare industries preferred; familiarity with pharmaceutical data (claims, APLD, specialty pharmacy, digital signals, promotional data) is a plus.

- Understanding of how data, analytics, and AI can be applied to solve commercial business problems.

Core Competencies

- Strong written and verbal communication skills, with the ability to translate complex analytics into transparent business insights.

- Solid project execution and organizational skills, with the ability to manage multiple analyses in parallel.

- Strong analytical thinking and problem-solving skills, with attention to detail and data quality.

- Hands-on expertise in applied statistics, analytics, and AI/ML techniques.

- Collaborative mindset with the ability to work effectively in a matrixed, stakeholder-driven environment.

- Curiosity and passion for learning, innovation, and continuous improvement in analytics.

Technical Skills (Preferred)

- Predictive and statistical analytics using Python and/or R; exposure to AI/ML and text analytics (e. g. , NLP, clustering, propensity models, uplift modeling) and to LLMs.

- Exposure to causal inference and incrementality methods (geo-experiments, matched markets, uplift modeling); awareness of MMx/adstock/response-curve concepts is a plus.

- Data visualization and dashboarding tools (e. g. , R Shiny, Dash, or similar platforms).

- Experience working in collaborative analytics environments (e. g. , Databricks, SharePoint, Git-based workflows); familiarity with cloud analytics platforms (Snowflake, Spark) is a plus.

- Familiarity with omnichannel, digital marketing, and commercial data sources, including CRM (e. g. , Veeva)

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Analyst, US Commercialization (Hyderabad)
🏢 Bristol-Myers Squibb
📍 Hyderabad