Senior Data Scientist

Salary: £65, per year

• We require a Masters or PhD in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.
• We require experience in data science, machine learning, or applied NLP.
• We require strong hands-on experience with search and retrieval systems, including lexical, vector, and hybrid approaches.
• We require strong hands-on experience with RAG pipelines and LLM-based systems.
• We require strong hands-on experience with evaluation methodologies for ML, IR, and GenAI.
• We require advanced programming skills in Python.
• We require experience with modern ML/NLP frameworks such as PyTorch, Hugging Face, Lang Chain, Lang Graph, or Haystack.
• We require experience working with Databricks or similar distributed data and ML platforms.
• We require a strong understanding of experimentation design and statistical analysis.
• We prefer a PhD in Computer Science, Data Science, Machine Learning, or a related field.
• We prefer experience working with large-scale datasets, including scientific, biomedical, or enterprise data.
• We prefer familiarity with scientific ontologies and metadata standards such as MeSH, UMLS, ORCID, and Cross Ref.
• We prefer exposure to production ML systems and MLOps practices.
• We prefer familiarity with data visualization and analytical tools such as Tableau, Power BI, matplotlib, seaborn, or similar.
• We prefer experience with human-in-the-loop evaluation or annotation workflows.
• We prefer publications or demonstrated applied research in IR, NLP, or generative AI.

• We lead the development and optimization of lexical, vector, and hybrid retrieval systems at scale.
• We help architect and improve RAG pipelines, including retrieval strategies, prompt design, and system orchestration.
• We drive experimentation with embeddings, re-ranking models, and retrieval architectures to improve relevance and user outcomes.
• We partner with engineering to ensure robust, scalable, and production-ready implementations.
• We define and evolve evaluation strategies for search and generative AI systems across our products.
• We design robust frameworks for IR evaluation, including NDCG, recall, and ranking quality.
• We design robust frameworks for GenAI evaluation, including grounding, faithfulness, and hallucination detection.
• We contribute to the development of evaluation datasets, gold standards, and annotation strategies.
• We guide and review experimental design, including offline evaluation and A/B testing, to ensure statistical rigor and validity.
• We contribute to responsible AI practices, including bias, fairness, and risk evaluation.
• We apply and adapt state-of-the-art techniques in NLP, embeddings, and generative AI to production use cases.
• We evaluate and integrate emerging technologies into our roadmap.
• We contribute to knowledge graph and semantic enrichment efforts that support retrieval systems.
• We collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems.
• We incorporate structured data, including datasets, chemical entities, genes, drugs, clinical trials, and patient outcomes, into AI-powered discovery pipelines.
• We advance our knowledge graph and metadata integration strategy to enable more context-aware retrieval.
• We apply cutting-edge research in information retrieval, NLP, embeddings, and generative AI to evolve our discovery and evaluation stack.
• We work closely with product, engineering, and domain experts to define and deliver impactful solutions.
• We communicate findings and recommendations clearly to both technical and non-technical stakeholders.
• We take ownership of projects from problem definition through…