Senior Principal Scientist Research ytics

Lifelancer
Slough
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Simulation Engineer (Data Science)

Principal Data Scientist & Machine Learning Researcher

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Principal Data Scientist

Data Science Lead / Manager

As a key member of the Research Analyticsgroup you will play a pivotal role in advancing UCBs drug discoveryand development efforts by applying advanced analytical techniquesto derive insights from complex data sets. This position offers aunique opportunity to contribute to the discovery and developmentof novel therapeutics in the areas of immunology and neurologyultimately making a meaningful impact on patientslives.

What youlldo

  • Lead thedesign and implementation of advanced data analytical strategies tosupport drug discovery and developmentprograms.
  • Analyze complex biological andchemical data sets to identify patterns trends and potentialtherapeutic targets and drugcandidates.
  • Collaborate closely withcrossfunctional teams including medicinal chemistry biologycomputational chemistry and bioinformatics to drive decisionmakingthroughout the drug discovery process.
  • Developand apply innovative computational approaches to enhance dataanalysis and interpretation.
  • Stay abreast ofthe latest developments in data science bioinformatics and machinelearning and integrate relevant methodologies into researchprojects.
  • Act as a subject matter expert inresearch analytics providing guidance and mentorship to junior teammembers.
  • Manage 12 direct reports and set theteam up for growth strengthen our digital talent and foster aculture of openminded cocreation of innovative solutions withinternal / external network of partners



Interested For this rolewere looking for the following education experience andskills

  • PhD.in computational biology bioinformatics computer science or arelated field with a minimum of 8 years of relevant industryexperience.
  • Proven track record of applyingadvanced data analytical techniques to drug discovery anddevelopment projects.
  • Expertise in statisticalanalysis machine learning and datavisualization.
  • Strong programming skills inlanguages such as (Linux / Python / Jupyter / Scikitlearn /Spotfire / etc.).
  • Experience working withlargescale biological and chemical data sets (e.g. biologicssequencing genomics proteomics highthroughputscreening).
  • Excellent communication andcollaboration skills with the ability to work effectively in amultidisciplinary team environment.
  • Priorexperience in the biopharmaceutical industry ispreferred.

Lifelancer () is a talenthiring platform in Life Sciences Pharma andIT. The platform connects talent with opportunities in pharmabiotech health sciences healthtech data science and ITdomains.

Please use the below Lifelancer linkfor job application and quickerresponse.

/jobs/view/20d5711d968f10e46daa89b1041729a8

RemoteWork :

No

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.