Lead Genomics Data Scientist

London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

We are excited to announce that we are currently seeking a talented Lead Data Scientist. This is a unique opportunity to lead a range of cancer genome analysis and interpretation projects in collaboration with both external researchers and industrial partners.

As the Lead Data Scientist, you will play a crucial role in enhancing customised cancer genome analysis within our research environment. You will actively contribute to the development and implementation of best practices for genome analysis, spearhead end-to-end complex genomic analysis projects, and conduct benchmarking exercises for tools used in processing, analysis, and interpretation of whole genome data.

We are looking for someone who has in-depth expertise in cancer genomics, with a solid understanding of tumor drivers and interpreting genomic data through targeted pathways. You should be proficient in utilising Python for efficient data processing and analysis and hands-on experience in developing high-quality and reusable code, with a strong command of Git and CI/CD practices.

To excel in this role, you should have a PhD degree or equivalent practical experience in an industry setting and experience in leading a cross-functional analytical team in an academic or industry environment. You should also have a good understanding of biomedical challenges and a commitment to producing high-quality code.

The company is committed to ensuring the adherence to high standards of relevance, excellence, and clinical safety in genomic analysis, aligning with the business accreditation requirements. You will collaborate seamlessly with internal and external stakeholders to guarantee the successful delivery of projects and employ and critically evaluate statistical genetics analysis methods to derive insights from large-scale genomic data.

If you are looking for a technical and scientific leadership role in the realms of cancer genome analysis, then this is the ideal opportunity for you. Apply now and take charge of managing and leading an inclusive, high-performing team, ensuring the presence of the right skills to fulfil the company mission.

Carbon60, Lorien & SRG - The Impellam Group STEM Portfolio are acting as an Employment Business in relation to this vacancy

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.