Lead Data Scientist and Machine Learning Lead

Understanding Recruitment
London
3 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Principal Data Scientist and Machine Learning Researcher

Lead Data Scientist

Lead Data Scientist - Recommender Systems

Lead Data Scientist

Lead Data Scientist

Lead Data Science Biomedical AI Location: London (Hybrid, 4 days on-site)
Compensation: Im working with a pioneering company at the cutting edge of AI-driven biomedical research , looking for a Lead Data Scientist to guide a world-class team applying machine learning to complex life sciences data.

This is a senior leadership role where youll define the data science vision and strategy across multiple programmes in areas like drug discovery, biomarker research, and health data analysis. The company is combining advanced AI with biological insight to accelerate discovery and improve patient outcomes on a global scale.

Set and deliver the data science strategy across biomedical and life sciences projects
Lead, mentor, and grow a multidisciplinary team of data scientists, ML engineers, and bioinformaticians
Design scalable pipelines and deploy ML models across genomic, proteomic, imaging, and clinical datasets
Ensure high standards of scientific rigor, validation, and reproducibility
Work closely with biologists, clinicians, and commercial partners to identify and develop impactful AI applications
Represent the company externally at leading AI and scientific conferences, building its profile within biomedical AI

Strong academic background in a relevant field such as Computer Science, Computational Biology, Bioinformatics, or Data Science (PhD preferred)
Proven track record leading data science teams in life sciences or biomedical research
Deep understanding of machine learning techniques and their application to biological data
Strong technical expertise in Python and modern ML frameworks
Excellent leadership, communication, and collaboration skills across scientific and technical disciplines

Lead at the forefront of AI and biomedical innovation
Shape how data science and machine learning drive discovery and patient outcomes
Hybrid setup with 4 days a week in their London HQ to support close collaboration and rapid progress

If youre a data science leader with deep expertise in biology or biomedicine and a passion for applying AI to real scientific challenges, this is a rare opportunity to make a lasting impact on the future of biomedical discovery.

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.