Agentic AI, Data Engineer COA Accelerator

IQVIA provides scientific services in clinical trials, real world evidence, and consulting, focusing on Clinical Outcomes Assessments (COAs) to incorporate the patient voice into medication development and commercialisation for life science and pharmaceutical companies. The COA Accelerator ecosystem expands AI-enabled capabilities relying on high-quality, structured, AI-ready data assets from clinical, regulatory, scientific, and proprietary content to support COA strategy workflows.

• Degree in computer science, data engineering, data science, information systems, bioinformatics, computational biology, engineering, or related technical field.
• Experience designing, building, and maintaining data pipelines for structured and unstructured data.
• Strong Python and SQL skills.
• Experience with APIs, relational databases, document stores, search indexes, cloud data platforms, and ETL/ELT workflows.
• Experience handling large volumes of text-heavy documents like PDFs, slide decks, reports, publications, regulatory files, or scientific literature.
• Strong understanding of data cleaning, normalisation, metadata management, document parsing, indexing, lineage, versioning, and auditability.
• Familiarity with data modelling for complex scientific, clinical, regulatory, or healthcare content.
• Ability to translate domain expert requirements into practical data structures, metadata models, and maintainable pipelines.
• Strong attention to detail to identify data quality issues affecting AI outputs or regulatory credibility.
• Ability to collaborate effectively with AI engineers, product managers, COA scientists, software engineers, security, legal, and commercial teams.
• Strong documentation skills covering data sources, transformations, lineage, access rules, data quality, and limitations.

• Design, build, and maintain data infrastructure supporting AI-enabled COA capabilities.
• Own ingestion, transformation, normalisation, enrichment, indexing, versioning, and governance of diverse data sources.
• Build ingestion pipelines for structured and unstructured sources including PDFs, Word documents, slide decks, spreadsheets, databases, APIs, and scientific publications.
• Transform raw material into standardised, searchable, AI-ready formats supporting evidence retrieval and expert review workflows.
• Develop repeatable processes for document parsing, OCR, text extraction, metadata enrichment, chunking, deduplication, versioning, indexing, and quality control.
• Provide technical support for platform knowledge layers including COA instrument metadata and regulatory precedent.
• Support integration of public data sources such as clinical trial registries, FDA labels, EMA EPARs, HTA records, scientific literature, and FDA guidance.
• Support ingestion of proprietary internal knowledge and expert content.
• Prepare data for retrieval-augmented generation workflows through chunking, embeddings, indexes, and metadata filters.
• Collaborate with AI engineers to improve retrieval precision, relevance, and citation accuracy.
• Implement hybrid retrieval approaches combining semantic search, keyword search, structured queries, and metadata filtering.
• Maintain traceability between AI outputs and source documents.
• Implement data quality controls identifying unreliable content.
• Maintain audit trails for source ingestion, transformations, access rights, and usage.
• Work with legal, security, compliance, product, and domain teams to ensure alignment with governance requirements.
• Collaborate across AI Engineering, Product, COA Science, and Software Engineering teams.

IQVIA maintains a zero tolerance policy for candidate fraud; all information submitted must be truthful and complete. Diversity, inclusion, and equal opportunity principles are core to the company's hiring and operational policies. Fluency in English is required.