Senior/Principal Data Scientist – Cross Indication

Relation Therapeutics
London, England
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist – Statistical Genetics

Relation Therapeutics London, United Kingdom
Permanent

Data Scientist

Faculty AI London, United Kingdom
Hybrid

Senior Machine Learning Engineer

Faculty AI London, United Kingdom
£0 pa Hybrid

Senior Cloud Engineer

Faculty AI London, United Kingdom
Hybrid

Senior Machine Learning Engineer

Faculty AI London, United Kingdom
£40,000 – £80,000 pa Remote

Senior Full Stack Software Engineer

Faculty AI London, United Kingdom
Hybrid
Posted
27 Jun 2025 (10 months ago)

Senior/Principal Data Scientist – Cross Indication

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. Our state-of-the-art wet and dry laboratories, located in the heart of London, provide an exceptional environment to foster interdisciplinarity and turn groundbreaking ideas into impactful therapies for patients.

We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. We cultivate innovation through collaboration, empowering every team member to do their best work and reach their highest potential.

By joining Relation, you will become part of an exceptionally talented team with extraordinary leverage to advance the field of drug discovery. Your work will shape our culture, strategic direction, and, most importantly, impact patients’ lives.

Opportunity

This is a unique opportunity for a data scientist to work on multi-omics data to drive transformative insights into drug discovery. As a member of the Cross Indication team, you will contribute to identifying and validating drug targets through advanced data analysis and innovative computational approaches.

The Cross Indication team collaborates across both Relations internal and partnership programmes, applying state-of-the-art computational methods to integrate diverse datasets. By combining biological insights with advanced data analytics, the team drives target discovery and validation initiative.

Your responsibilities

  • Develop and implement computational workflows for analysing multi-omics data.

  • Perform data integration to uncover disease mechanisms and identify actionable targets.

  • Design statistical models for analysing transcriptomics and other omics datasets.

  • Collaborate closely with experimental and machine learning teams to validate computational insights.

  • Present findings and methodologies to internal stakeholders and contribute to publications.

Professionally, you have

  • PhD in computational biology, bioinformatics, or a related quantitative field.

  • Extensive experience in multi-omics data analysis, including transcriptomics.

  • Proficiency in Python and familiarity with high-performance computing environments.

Desirable knowledge or experiences

  • Familiarity with single-cell transcriptomics or patient-derived datasets.

  • Experience working in interdisciplinary teams within biotech or pharma settings.

  • Knowledge of machine learning techniques applied to biological data.

  • A background in statistical modelling and algorithm development.

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. The patient is waiting!

Relation is a committed equal opportunities employer.

RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.