Manager, Real World Data

Job Summary

Position: Manager - Real World Data

Grade: EG 110

Job Family: Information Technology / Applications Development

Career Level: Individual Contributor with domain and technical leadership scope

Location: Hyderabad, India

Position Summary

At BMS, digital innovation and Information Technology are central to our vision of transforming patients lives through science. To accelerate our ability to serve patients around the world, we must unleash the power of technology. We are committed to being at the forefront of transforming the way medicine is made and delivered by harnessing the power of computer and data science, artificial intelligence, and other technologies to promote scientific discovery, faster decision making, and enhanced patient care.

As a Manager - Real-World Data, based out of BMS Hyderabad, you sit within the Portfolio and Strategy Operations - Real-World Data Strategy and Operations team, and you are accountable for building the applications, data products and AI-enabled services that make real-world data usable at enterprise scale across Research & Development, Medical and Market Access. This is a hands-on senior individual contributor role: you will design and deliver production software and data pipelines, embed machine learning and generative AI capabilities into those products, and set the standards that the wider RWD team builds to.

The successful candidate combines deep real-world data domain fluency with strong data engineering craft - comfortable with structured and unstructured healthcare data, cloud-native architecture, common data models such as OMOP, and modern AI engineering patterns. You will operate with significant technical autonomy, translate ambiguous scientific and commercial questions into engineered solutions, influence platform roadmaps, and lead delivery across globally distributed, cross-functional teams while mentoring less experienced developers and analysts.

Key Responsibilities

• Application design and delivery Architect, develop, test and deploy full-stack applications, APIs and services that make real-world data discoverable, queryable and analysis-ready for scientific and commercial users.
• End-to-end engineering ownership Translate business and scientific requirements into technical designs, epics and sprint deliverables; own code quality, automated testing, peer review, CI/CD, release management and production support for the products you deliver.
• RWD data strategy and landscaping at scale Build and optimize ingestion, curation and transformation pipelines for EHR, open and closed claims, registry, oncology, laboratory, genomic and tokenized linked datasets to enable faster data landscaping
• Common data models and interoperability Deliver and extend OMOP CDM / OHDSI-aligned standardization, vocabulary mapping, terminology services and data quality frameworks so that cohorts and analyses are reproducible across vendors, studies and therapeutic areas.
• Analytical Ready Datasets and data products Design reusable, well-documented analytical ready datasets and data products with clear contracts, metadata, lineage and stewardship, replacing bespoke one-off extracts.
• AI and generative AI capabilities Embed machine learning, NLP and LLM-based services - retrieval-augmented generation, semantic and natural-language cohort search, clinical note and document extraction, summarization and agentic workflows - into RWD applications, with evaluation, guardrails and human-in-the-loop review designed in from the start.
• Automation and self-service Systematically remove manual analyst effort through automation, reusable components and self-service tooling; identify, quantify and deliver cycle-time and cost-optimization opportunities.
• Governance, privacy and compliance by design Build to BMS standards for computer system validation and GxP, 21 CFR Part 11, HIPAA and GDPR, contractual data-use restrictions, PII and PHI handling, tokenization, access control and audit traceability.
• Technical leadership Set coding standards, design patterns and architectural direction for the team; lead design and code reviews; serve as the senior technical point of contact between Hyderabad and US-based product, data science, platform and enterprise architecture teams.
• Mentoring and capability building Coach developers and analysts, run knowledge-sharing and enablement sessions, and raise team capability in modern data engineering, cloud and AI engineering practice.
• Vendor and partner engagement Work with RWD vendors and third-party administrator or system-integrator partners on technical onboarding, data delivery, defect resolution and evaluation of new data assets, platforms and AI tooling.
• Stakeholder partnership Collaborate with epidemiologists, biostatisticians, clinicians, HEOR, regulatory and market access colleagues to shape study feasibility, platform roadmaps and prioritization, and communicate technical trade-offs in terms they can act on.

Qualifications & Experience

Required

• Bachelors or Masters degree in Computer Science, Engineering, Health or Bio-Informatics, Biostatistics, Epidemiology or a related quantitative discipline
• Typically, 8+ years of hands-on application development and data engineering experience, including at least 3 years delivering real-world data or healthcare data solutions in biopharma, payer, provider or health technology settings
• Advanced programming skills in Python and SQL, with production experience in at least one modern application stack, and disciplined use of Git-based version control and CI/CD (GitHub Actions, Jenkins or equivalent)
• Demonstrated experience operating large-scale distributed data pipelines on cloud (AWS preferred) and lakehouse or Spark platforms such as Databricks; working experience with Domino, Snowflake or equivalent computing and warehouse environments
• Strong working knowledge of RWD source types - EHR, open and closed claims, registries, laboratory, EMR-derived oncology and patient tokens - including their strengths, limitations and licensing constraints
• Hands-on experience with healthcare data standards and common data models: OMOP CDM, the OHDSI toolchain, FHIR, and clinical vocabularies such as ICD, SNOMED, RxNorm and LOINC
• Experience implementing data quality, observability, monitoring and automated testing frameworks for both data and applications
• Sound understanding of the regulatory and compliance landscape for RWD and RWE, including the FDA Real-World Evidence programme, EMA guidance, GxP and computer system validation, and data privacy regulation
• Experience of delivering in Agile or Scrum teams alongside product owners, and working effectively across distributed global time zones
• Excellent written and verbal communication skills, with the ability to explain architecture