Data Scientist – NLP & Generative AI

Job Title: Data Scientist – NLP & Generative AI (Contract) Location: Hybrid (London-based) Contract Type: 6-Month Contract (Inside IR35) Overview A leading global organisation is seeking a Data Scientist with expertise in Natural Language Processing (NLP) and Generative AI to join their advanced R&D team. This role will focus on developing and deploying Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and domain-specific GenAI solutions to support high-impact internal use cases. Key Responsibilities • Design, implement, and maintain scalable NLP and GenAI pipelines, including data processing and evaluation. • Conduct advanced data analysis on real-world datasets to extract actionable insights. • Stay current with cutting-edge research in LLMs and NLP, proposing innovative methodologies that deliver business value. • Develop and optimize RAG systems and retrieval pipelines (chunking, embedding, re-ranking, evaluation). • Lead and participate in experiments, including writing reusable code and organizing results. • Collaborate with cross-functional teams to prioritize research and deliver impactful solutions. • Engage with stakeholders to gather requirements, plan project scopes, and ensure timely delivery. Required Qualifications • MSc in Computer Science, Electrical Engineering, or a related technical field — or 5+ years of relevant experience. • Strong understanding of modern NLP, LLMs, transformer architectures, prompt engineering, RAG, agentic architectures, and evaluation methodologies. • Proficiency in Python for AI model development and debugging. • Excellent communication skills and a collaborative mindset. • Passion for cross-disciplinary collaboration and delivering both scientific and business impact. Preferred Qualifications • Experience in R&D with a focus on NLP/GenAI. • Hands-on experience building and evaluating RAG and GenAI systems in production-like environments. • Experience with training, fine-tuning, or experimenting with foundation models. • Contributions to publications or open-source projects. • Experience mentoring or collaborating with researchers.