Join Clinical AI to build production‑ready LLM and GenAI solutions that accelerate drug development and improve patient care across global R&D.
Your new role
As a Lead Data Scientist in Clinical AI you will design, develop and deploy LLM‑based and ML solutions for content retrieval, generation, summarization and inference to support cross‑functional healthcare and drug‑development challenges. You will bridge data science and software engineering-architecting services, designing APIs, building backend systems and integrating models while ensuring performance, safety, privacy and regulatory compliance.
Day‑to‑day your tasks will include:
Design and develop production‑ready LLM/GenAI solutions for retrieval‑augmented generation (RAG), summarization and inference to address clinical and R&D use cases. Build and integrate backend services, APIs and data pipelines (embeddings, vector DBs, knowledge bases) and support end‑to‑end deployment using cloud platforms and containerization. Fine‑tune and evaluate models (supervised, LoRA/PEFT, prompt engineering), implement monitoring and testing frameworks for performance, fairness, hallucination rate, latency and cost. Optimize models and systems (quantization, distillation, caching), and operationalise with LLM‑Ops tools and CI/CD best practices for stable, secure production use. Ensure compliance with internal/external AI governance, data protection and regulatory requirements (anonymization, access controls, audit logging) and produce technical documentation and runbooks Collaborate with internal and external stakeholders across Data Science, Engineering, Medical and Regulatory teams to align on solutions, publish outcomes and drive technology roadmap adoption. Stay current on LLM research, trends, best practices and technology roadmap, and pursue publications through R&D relevant activities.
Your new department
You will join the Clinical AI & Analytics team in R&D, working across the Target Discovery, Clinical Development and Medical functions. Our team blends advanced machine learning research with production engineering to create trustworthy AI systems that support discovery, development and patient‑facing insights.
Your skills & qualifications
To succeed in this role, you should:
Hold a PhD, Master’s or Bachelor’s degree in Computer Science, Computer Engineering, Computational Biology, Engineering or a related quantitative discipline (PhD preferred). Have strong practical experience in LLMs / generative AI: model selection, fine‑tuning (LoRA, PEFT), prompt engineering, evaluation and observability. Possess software engineering experience from architecture design to Infrastructure as Code (IaC), with hands‑on experience in cloud platforms, containers and microservices and automating serverless, event-driven pipelines in cloud platforms. Be experienced building data pipelines and retrieval systems (embeddings, vector DBs, knowledge bases) to support RAG and document understanding. Demonstrate competence implementing testing, monitoring and optimisation for model performance, fairness and cost; familiar with LLM‑Ops tools (e.g., LangChain, LlamaIndex, Langfuse) is advantageous. Have excellent collaboration and communication skills to work with cross‑functional teams, translate technical concepts for stakeholders, and ensure regulatory and privacy requirements (GDPR) are met. Have experience with implementing CI/CD best practices, including API development, MCP implementation, cloud-based distributed systems, containerization, integration, test automation and monitoring, particularly in stateful LLM system designs. Have a strong publications record in applied LLM/ML research areas
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