About IQVIA
IQVIA provides scientific services spanning clinical trials, real world evidence, and consulting in all areas of the product lifecycle. Our Clinical Outcomes Assessments (COAs) organisation leads the industry in generating data to ensure that the patient voice is incorporated into the development and commercialisation of medication and other drug/non-drug interventions. We focus on understanding and meeting the needs of our clients – mostly life science/pharmaceutical companies – through the application of broad consulting expertise and technical scientific knowledge to conduct scientifically rigorous research.
Role & Responsibilities
- Design, develop, and optimise AI capabilities that support COA strategy generation, evidence synthesis, recommendation development, and expert review workflows.
- Own the technical implementation of AI reasoning, retrieval-augmented generation, model orchestration, prompt architecture, evaluation pipelines, and domain adaptation for COA-related use cases.
- Build AI workflows that can interpret therapy area, indication, target product profile, study phase, endpoint objectives, target population, regulatory context, and trial design considerations.
- Develop AI capabilities that generate structured, evidence-backed COA strategy recommendations, including recommended instruments, endpoint considerations, rationale, evidence gaps, and supporting source material.
- Design and optimise retrieval pipelines across structured and unstructured sources, including COA libraries, psychometric evidence, scientific literature, regulatory labels, HTA documents, clinical trial records, and internal consulting outputs.
- Implement semantic search, hybrid search, metadata filtering, reranking, source attribution, context-window optimisation, and citation-supporting workflows.
- Evaluate when to use foundation models, fine-tuned models, smaller specialist models, embeddings, rerankers, deterministic rules, or hybrid approaches.
- Develop mechanisms that allow AI outputs to distinguish between strong evidence, weak precedent, outdated information, unsupported claims, and areas requiring expert judgement.
- Create and maintain model evaluation frameworks to assess factuality, retrieval relevance, citation accuracy, recommendation consistency, clinical reasoning quality, and hallucination risk.
- Partner with COA scientists, product managers, data engineers, software engineers, security teams, legal stakeholders, and commercial teams to ensure AI capabilities are scientifically credible, secure, explainable, and commercially useful.
- Support demos, prototypes, pilots, and client-facing proof-of-concept work where AI functionality needs to be explained clearly to scientific, commercial, or technical audiences.
- Document model behaviour, assumptions, known limitations, evaluation results, decision logic, and change history.
- Contribute to AI governance practices for responsible AI use in clinical research, COA strategy, and regulated decision-support contexts.
Individuals joining us are assured of a rewarding and progressive career in patient-focused research. You’ll have the opportunity to address challenging client issues, across multiple geographies, with a hands-on influence in developing and delivering innovative solutions. We operate in a truly multi-cultural, collegial and collaborative work environment that is rich in development and growth.
Skills & Qualifications
- Degree in computer science, machine learning, artificial intelligence, data science, computational linguistics, biomedical informatics, bioinformatics, engineering, or a related technical field.
- Experience building AI, NLP, LLM-powered, or machine learning applications in production environments.
- Practical experience with retrieval-augmented generation, embeddings, vector databases, semantic search, prompt engineering, model evaluation, and LLM orchestration.
- Strong Python skills and familiarity with modern AI/ML frameworks, APIs, testing practices, version control, and deployment workflows.
- Experience working with unstructured scientific, clinical, regulatory, healthcare, or research-related data.
- Ability to translate expert reasoning into technical implementation, especially where domain logic is nuanced, evidence-based, and not fully deterministic.
- Strong understanding of AI output quality risks, including hallucination, overconfidence, weak source grounding, inconsistent reasoning, and unsupported recommendations.
- Strong analytical judgement and ability to identify when AI-generated outputs are incomplete, unsupported, misleading, or require human review.
- Ability to collaborate effectively with technical and non-technical stakeholders, including COA scientists, product teams, data engineers, software engineers, security stakeholders, and commercial teams.
- Excellent documentation skills, including the ability to explain AI system behaviour, assumptions, limitations, validation results, and governance considerations.
Additional Requirements
- Experience with life sciences, clinical research, regulatory strategy, COAs, patient-reported outcomes, medical evidence synthesis, or clinical decision support is strongly preferred.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, Haystack, Semantic Kernel, DSPy, or equivalent tooling.
- Experience with vector databases and search technologies such as Azure AI Search, Pinecone, Weaviate, Milvus, Qdrant, OpenSearch, Elasticsearch, or similar platforms.
- Familiarity with model evaluation tools, LLM observability platforms, automated evaluation frameworks, benchmarking approaches, and continuous improvement loops.
- Experience with fine-tuning or adapting open-source models using LoRA, QLoRA, instruction tuning, supervised fine-tuning, or related methods is desirable.
- Experience with cloud environments such as Azure, AWS, or GCP, preferably in enterprise, healthcare, life sciences, or regulated settings.
- Understanding of GDPR, data privacy, secure AI deployment, proprietary data handling, access controls, auditability, and model governance.
- Ability to work independently in a remote or hybrid environment while collaborating across global teams.
- Significant experience leveraging AI tools for work such as Cursor, Claude Code, Codex, or other equivalent tools.
- Fluency in English.
