Lead Data Scientist
Novo Nordisk
full-time
Posted on:
Location Type: Office
Location: London • United Kingdom
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Job Level
Tech Stack
About the role
- 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
Requirements
- Hold a PhD, Master’s or Bachelor’s degree in Computer Science, Computer Engineering, Computational Biology, Engineering or a related quantitative discipline (PhD preferred)
- Strong practical experience in LLMs / generative AI: model selection, fine‑tuning (LoRA, PEFT), prompt engineering, evaluation and observability
- 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
- Experienced building data pipelines and retrieval systems (embeddings, vector DBs, knowledge bases) to support RAG and document understanding
- Competence implementing testing, monitoring and optimisation for model performance, fairness and cost; familiar with LLM‑Ops tools (e.g., LangChain, LlamaIndex, Langfuse) is advantageous
- 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
- 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
- Strong publications record in applied LLM/ML research areas
Benefits
- Collaborative environment that values scientific rigour
- Continuous learning opportunities
- Responsible innovation culture
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
LLMsgenerative AImodel selectionfine-tuningprompt engineeringdata pipelinesretrieval systemsInfrastructure as Codetestingmonitoring
Soft skills
collaborationcommunicationcross-functional teamworktechnical translation
Certifications
PhDMaster's degreeBachelor's degree