Senior Data Scientist - Drug Discovery - fully remote in UK

Hays
Liverpool
8 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Engineer

Data Engineer

Your new company
You will be joining an expanding consultancy focused on supporting innovation in the pharmaceutical and biotech industry. Its specialist research division is dedicated to solving complex biological problems through advanced statistical modelling and ML/AI. They have an experienced team with a strong track record and are looking for an extra person to join them to work on statistical method development and application to real-world drug discovery & development problems.


Your new role
As a Senior Data Scientist, your work will centre on methodological innovation. You will:

  • Design and implement novel statistical approaches to interrogate large-scale genomic datasets
  • Develop new models to quantify genetic contributions to disease and complex traits
  • Evaluate and refine existing analytical frameworks to improve accuracy and interpretability
  • Ensure scientific rigour and reproducibility in all method development
  • Translate complex statistical outputs into meaningful insights for technical and non-technical audiences
  • Collaborate with engineering teams to embed new methods into scalable data pipelines
  • Contribute to peer-reviewed publications that showcase methodological advancements
  • Stay ahead of emerging techniques in statistical genetics and bioinformatics, integrating them into ongoing research


While pharma/biotech or consultancy industry experience is preferred, this role could also suit a recent PhD graduate or junior post-doc researcher with strong statistical method development.

The role can be fuly home based, or you can work from one of the company's offices across the UK.


What you'll need to succeed

  • PhD (or Master's with substantial experience) in statistics, maths, physics, data science, computing, statistical genetics or a related field with a strong methodological focus (or equivalent experience)
  • Demonstrated ability to create and validate new statistical / analytical models or workflows
  • Strong programming skills in R or Python, with experience in statistical libraries and bioinformatics tools
  • Familiarity with biobank-scale datasets and genomic databases
  • Experience with cloud platforms and scalable computing environments
  • A publication record that reflects methodological contributions to the field
  • Good communication skills, especially in explaining statistical concepts to diverse audiences



What you'll get in return
You'll be joining a highly experienced team doing cutting-edge work to support drug discovery & development efforts at a wide range of pharmaceutical and biotech companies. As well as lots of opportunities to develop your skills and career, this role offers a good package and the chance to make a significant impact.


What you need to do now
If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV, or call us now.
If this job isn't quite right for you but you are looking for a new position, please contact us for a confidential discussion on your career.

Keywords: Statistical, Genetics, Bioinformatics, Genomics, Data, Scientist, Lead, Senior, GWAS, Polygenic, Risk, Score, Mendelian, Randomisation, Causal, Inference, Computational, Biology, Genetic, Epidemiology, Variant, Annotation, Pathway, Method, Enrichment, Protein, Interaction, Networks, Biobank, Research, Modelling, Development

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 Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science 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 Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.