Senior Genetic Informatician

Novo Nordisk A/S
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Climate Data Scientist

Senior Machine Learning Engineer, Gen AI

Managing Consultant - C&M - Data Science

Analyst, Artificial Intelligence Policy (Fixed Term Contract)

Senior Machine Learning Engineer

Machine Learning Engineer Python AWS

For over 100 years we have been driving change to defeat diabetes, but we know that what got us here today is not necessarily what will make us successful in the future. We are now transforming our business and taking our expertise into new territories including obesity and rare blood and endocrine diseases.

Our story is one of incredible growth and success, which has culminated in receiving many prestigious awards, such as Best Places to Work and Vitality – Britain’s Healthiest Workplace.

The Position

As a Senior Genetic Informatician at Novo Nordisk, you will have an opportunity to work with genetic scientists across the Genetics Centre of Excellence (CoE) to build the tools and infrastructure that enable our human genetic analyses.

As part of your role, you will:

Develop computational tools, working collaboratively with colleagues and ensuring good coding and software development practices are followed throughout. Improve genetic analyses by evaluating and integrating novel methods and data from scientific literature or other sources, developing novel solutions where necessary. Identify and help champion best practices for scientific and computational work in the Genetics CoE.

We offer a hybrid-working model for this role, where office presence in Oxford or London would be expected 2-3 times per week. There is also a possibility for the role to be based in Copenhagen, Denmark.

Qualifications

We are looking for a candidate with strong interpersonal skills who can communicate clearly with both scientific and computational experts. Experience working within an agile/Scrum environment and in the biotech industry would be a plus.

The qualifications needed for the role include:

PhD in bioinformatics, computational biology, computer science, machine learning or related field with experience in biology and genetics applications Substantial experience in genetics research and genetic analyses Substantial experience developing tools and pipelines in programming languages such as Python and R Experience working in high performance computing environment (HPC, Slurm) and cloud environments such as AWS or Azure Experience developing scientific workflows and pipelines (e.g. with Nextflow) following good software development practices (e.g. using Git for version control)

About the Department

You will join our Computational and Statistical Genomics (CSG) team, which is part of the Human Genetics Centre of Excellence (CoE) and based primarily in Oxford.

The focus of the Genetics CoE is to use data science and human genetics to discover and develop new drug targets and biomarkers through a range of human centric approaches e.g., high-throughput genetic discovery screens; Mendelian randomization; pLoF variant screens and precision medicine approaches. It is anchored in the newly established Digital Science & Innovation (DSI) organisation within Research & Early Development at Novo Nordisk, as part of the AI and Digital Research unit. We participate in drug development projects across the value chain, from early discovery to pre-clinical development, engaging in therapy areas such as type 2 diabetes, chronic kidney disease, cardiovascular disease, metabolic dysfunction-associated steatohepatitis (MASH), obesity, and rare endocrine and blood disorders.

Our department offers a dynamic and collaborative atmosphere where you will have the chance to work with cutting-edge genetics research and researchers. Our team is committed to driving innovation and excellence in genetic analyses. You will work on interesting data sets and set new ways of working for the genetics department by building the tools they will use in the future.

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.