Senior Data Scientist I - LeapSpace

Description

The team sits inside the Platform Data Science group, a central AI and data science unit that builds the foundational AI technology behind LeapSpace, the company's AI research assistant, and its wider Search and AI Platform.

The group works across search and retrieval, generative AI and LLM applications, AI evaluation and experimentation, semantic enrichment and knowledge systems, and scalable AI platforms and intelligent workflows.

This is a senior applied AI role focused on prototyping and building AI features for LeapSpace, including combining large language models with trusted scientific data and improving AI-assisted research tools. You will work across retrieval, generative AI, reasoning workflows, evaluation and experimentation. The role suits someone with strong hands-on applied AI, NLP, retrieval and LLM experience who likes to prototype quickly and turn new AI techniques into products that scale.

Responsibilities

• Prototype and build LLM-powered research workflows, such as scientific question answering, literature summarisation, semantic exploration, research insight generation and citation-aware reasoning.
• Design and refine agentic, multi-step AI workflows using LangGraph and related orchestration tools.
• Apply current methods in NLP, generative AI, embeddings, retrieval-augmented generation and AI reasoning.
• Test new AI models, tools and frameworks quickly to find product opportunities.
• Turn applied research into production-ready solutions that help researchers work faster and trust the output more.
• Run experiments on prompt design, context handling, grounding and reducing hallucinations.
• Bring scientific metadata, ontologies and knowledge assets into AI workflows.
• Design and tune lexical, vector and hybrid search and retrieval pipelines.
• Build and improve RAG systems that combine LLMs with trusted scientific and biomedical content.
• Experiment with embeddings, re-ranking, chunking and retrieval orchestration to improve relevance and answer quality.
• Build scalable semantic search and knowledge discovery workflows and work with engineers to put them into production.
• Create and maintain evaluation frameworks for search and AI systems, covering IR metrics such as NDCG, recall and precision, and LLM and RAG metrics such as grounding, faithfulness and hallucination detection.
• Design offline evaluations and take part in online experiments and A/B tests.
• Build and maintain evaluation datasets, benchmarks and annotation approaches.
• Run rigorous experiments to measure system gains and user impact, and support responsible AI work on quality, reliability and trust.
• Work with product managers, engineers, UX researchers and domain experts to deliver AI features.
• Explain complex technical findings to stakeholders as clear recommendations, and help shape the technical strategy and roadmap for LeapSpace AI.

Requirements

• A master's or PhD in computer science, data science, machine learning, NLP, information retrieval or a related field.
• Significant experience in applied AI, machine learning, NLP or information retrieval.
• Strong hands-on experience building LLM applications and generative AI systems.
• Experience with RAG pipelines and retrieval systems.
• Experience with lexical, vector and hybrid search architectures.
• Experience evaluating IR and generative AI systems.
• Advanced Python programming.
• Experience with AI and ML frameworks and tools such as PyTorch, Hugging Face, LangChain, LangGraph and Haystack.
• Experience with Databricks or similar distributed data and ML platforms.
• Strong grasp of experiment design, evaluation frameworks and statistical analysis.
• Proficiency with data visualisation and analysis tools such as Tableau, Power BI, matplotlib and seaborn.
• Preferred: experience building AI assistants, agentic workflows or conversational AI systems.
• Preferred: experience with large-scale search, ranking or recommendation systems.
• Preferred: familiarity with scientific, biomedical or scholarly datasets.
• Preferred: experience with knowledge graphs, ontologies or semantic enrichment systems.
• Preferred: exposure to production ML systems and MLOps practices.
• Preferred: publications or applied research contributions in NLP, IR, search or generative AI.
• Preferred: experience building AI systems in regulated, high-trust or content-rich domains.

Conditions

The company offers flexible working hours, so you can arrange your day around when you work best.

The company is fair and accessible in its hiring; reasonable adjustments are available on request.

Benefits

• Wellbeing initiatives, shared parental leave, study assistance and sabbaticals to support both daily commitments and long-term goals.
• Country-specific benefits, available through the company's location-based benefits page.