Principal / Lead Computational Biologist - NLP

hays-gcj-v4-pd-online
London
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer (GCP)

Principal Data Engineer (GCP)

Senior Machine Learning Engineer

Principal Data Engineer

Principal Data Engineer (MS Azure)

Principal GCP Data Engineer

Your newpany

You will be joining a highly innovative biotech in London developing novel drugs across a range of disease areas as they go into their next phase of growth.

Thispany has a proprietary platform that integrates a range of algorithms and target identification tools to find and validate drug targets and is looking to add an experiencedputational biologist with deep expertise of ML / AI approaches to their well-established team to help progress their pipeline.

They have a highly collaborative and social atmosphere, with a mix of experience and backgrounds and a great working atmosphere in highly modern offices.

Your new role

You will play a leading role in the development and validation of thepany’sputational platform, working with a number of internal stakeholders and senior management to drive both technicalputational and drug discovery projects forward.

You will manage and conduct projects to support drug discovery efforts by developing and implementing additional functionality/capabilities for the existing platform anding up with entirely new tools & approaches; primarily within NLP / LLMs, though this is not exclusive.

A key part of this role is to interact closely with the biology, chemistry, business analytics and bioinformatics /putational biology teams to understand requirements and then design new or refine existing platforms to support their work.

There will be plenty of support from the wider team but the expectation is you will act as a technical expert/lead and have significant input into the technical details.

Depending on the level of applicants, this role can be at Principal or Associate Director level, with line management duties for more senior candidates if that is of interest. The role can also stay as an individual contributor if preferred.

What you'll need to succeed

Aside from the ability to work both independently and as part of a wider team, you should ideally have:

An MSc / PhD (or equivalent experience) in maths, biology, bioinformatics, physics, statistics, data science or a related subject.
Candidates without a higher degree but with a strong background in the utilisation/application of AI within the biotech/pharma industry are also encouraged to apply. Hands-on expertise of designing, managing and deliveringputational projects within a biotech / pharma / drug discovery setting The ability to analyse large scale biological (preferably genetic/genomic) data sets Strong programming skills in Python and/or R, C++, Java or similar, eg for developing tools, packages, models, algorithms or similar, coupled with a knowledge of good software development practices, eg version control, DevOps, etc Experience of working closely with cross-disciplinary teams to understand requirements and translate these into workflows / tools A track record of applying Machine Learning or Deep learning approaches to problems, with a good understanding of NLP / Large Language Models and libraries such as TensorFlow, Keras, etc Goodmunication and interpersonal skills A real interest in solving technical problems

For more senior candidates, a track record of leadership – either from direct line management or matrix management – of teams is required.

What you'll get in return

You will have the opportunity to make a significant impact on the business, working on cutting-edge projects that aim to improve the lives of millions of people and ultimately help drive a new approach to drug discovery.

You will also get the chance to grow your skills and career in a supportive and collaborative environment that values innovation, creativity and excellence and is highly mission driven.

On top of this, they have a great, modern office facility in London with state-of-the-art equipment and resources.

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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.