Senior/Principal Data Scientist – Cross Indication

Relation Therapeutics Limited
London
8 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Senior & Principal Data Architect

Principal Data Architect

Principal Data Architect

Senior Data Engineer - Azure

Principal Data Engineer

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.