relationrx - Snr/Principal Data Scientist – Turing (LLMs, Agents, Data & Scientific Reasoning)
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Requirements
• A PhD or equivalent experience in computational biology, machine learning, data science, or a related quantitative field. • Strong practical experience working with large language models and modern AI tooling, including prompt engineering, tool use, retrieval systems, or agent-based architectures. • Strong programming skills in Python, with experience working with large-scale datasets and modern ML tooling. • Experience designing evaluation frameworks, benchmarks, or testing methodologies for machine learning systems. • An understanding of biological data and the drug discovery process, or a strong motivation to work in this domain. • Experience applying LLMs or AI systems to scientific or biomedical data. • Familiarity with literature mining, knowledge integration, or scientific knowledge representation. • Experience building multi-step or agent-based reasoning systems. • A background in computational biology, genomics, or drug discovery. • Experience working in cross-disciplinary teams combining machine learning and biology. • PERSONALLY, YOU ARE • A collaborative team player who enjoys working in interdisciplinary environments. • A clear communicator who can translate complex ideas across computational and biological teams. • Curious, pragmatic, and motivated by solving meaningful scientific problems. • Excited to work on technology that can have real impact on patients. • Humble, thoughtful, and eager to learn. • Relation Therapeutics is a committed equal opportunities employer. • RECRUITMENT AGENCIES: Please note that Relation Therapeutics does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.
Responsibilities
• Develop and deploy LLM-driven systems to support scientific discovery, including literature analysis, hypothesis generation, and knowledge extraction. • Design and implement agentic workflows that integrate multiple tools, models, and data sources to support biological reasoning and research workflows. • Work with large, heterogeneous biomedical datasets (omics, literature, knowledge bases, and clinical data) to generate insights relevant to target discovery. • Develop evaluation frameworks and benchmarks to assess the performance and reliability of AI systems in scientific contexts. • Collaborate closely with biologists, computational scientists, and engineers to translate biological questions into computational solutions. • Prototype and productionise data pipelines, models, and analysis workflows that support ongoing discovery programmes. • Contribute to the development of new computational approaches for reasoning over biological knowledge and evidence. • A PhD or equivalent experience in computational biology, machine learning, data science, or a related quantitative field. • Strong practical experience working with large language models and modern AI tooling, including prompt engineering, tool use, retrieval systems, or agent-based architectures. • Strong programming skills in Python, with experience working with large-scale datasets and modern ML tooling. • Experience designing evaluation frameworks, benchmarks, or testing methodologies for machine learning systems. • An understanding of biological data and the drug discovery process, or a strong motivation to work in this domain.
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