Senior/Principal Data Scientist – Turing (LLM’s, KGs & Graph)
London
About Relation
Relation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning to drive disease understanding—from cause to cure.
This year, we embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. By combining our cutting-edge ML capabilities with GSK’s deep expertise in drug discovery, this partnership underscores our commitment to pioneering science and delivering impactful therapies to patients.
We are rapidly scaling our technology and discovery teams, offering a unique opportunity to join one of the most innovative TechBio companies. Be part of our dynamic, interdisciplinary teams, collaborating closely to redefine the boundaries of possibility in drug discovery.
Opportunity
Be part of the innovative Turing team, where you will leverage advanced computational technologies such as large language models (LLMs) and knowledge graphs (KGs). As a Senior/Principal Data Scientist, you will play a pivotal role in driving data-driven drug discovery through these cutting-edge approaches.
The Turing team integrates computational and biological expertise to utilise knowledge graphs and Large Language Models in drug discovery. By connecting diverse datasets and computational outputs, the team enhances decision-making in target prioritisation and therapeutic development.
Your responsibilities
- Develop and apply Graph and LLM base solutions/methods for drug target identification and validation.
- Integrate insights from omics and clinical data using graph-based models.
- Collaborate with interdisciplinary teams to align computational approaches with research goals.
- Design workflows to extract actionable insights from large-scale datasets.
- Advance methodologies for computational drug discovery using graph-based techniques.
Professionally, you have
- PhD in computational biology, data science, or a related field.
- Expertise in LLMs, KGs, multi-agent reasoning systems or graph-based computational techniques.
- Proficiency in Python and frameworks for handling large-scale datasets.
- Strong understanding of the drug discovery pipeline and computational modelling.
Desirable knowledge or experiences
- Experience integrating graph-based approaches with multi-omics data.
- Familiarity with graph database systems and their applications in biology.
Personally, you are
- Inclusive leader and team player.
- Clear communicator.
- Driven by impact.
- Humble and hungry to learn.
- Motivated and curious.
- Passionate about making a difference in patients’ lives.
Join us in this exciting role, where your contributions will directly impact advancing our understanding of genetics and disease risk, supporting our mission to deliver transformative medicines to patients. Together, we’re not just conducting research—we’re setting new standards in the fields of machine learning and genetics.
#J-18808-Ljbffr
