Research Assistant, Wellcome Ecology/Data Science

Research Assistant, Wellcome Ecology/Data Science

Join to apply for the Research Assistant, Wellcome Ecology/Data Science role at Natural History Museum.

About Us

We are a world‑class visitor attraction and leading science research centre. We use the Museum’s unique collections and our unrivalled expertise to tackle the biggest challenges facing the world today. We care for more than 80 million objects spanning billions of years, welcome over five million visitors annually and receive 16 million visits to our website. Today the Museum is more relevant and influential than ever, attracting people from a range of backgrounds to work for us and to find new ways of doing things.

About the Role

Understanding and communicating how climate and land‑use change affect human societies is essential for driving lasting shifts in our collective behaviour. The post will be based in a lab that studies the impacts of global change processes – such as climate, land‑use, and demographic change – on biodiversity and human health. For example, we investigate how zoonotic pathogens spill over between people and animals in regions undergoing agricultural expansion to understand how disease risks may evolve as natural habitats are increasingly converted to cropland. A key aspect of this work involves making effective use of large biodiversity databases, such as GBIF, to link ecological processes to disease dynamics while developing statistical methods to account for biases in these underlying data.

Using statistical and computational tools, including disaggregation regression, the postholder will develop methods to infer movement patterns from static observations to determine habitat‑use preferences from large‑scale occurrence datasets. They will also contribute expertise in reproducible coding workflows, data management, and machine learning, supporting the wider lab. In addition, the postholder will help publish ongoing research and will be encouraged and supported to pursue new research directions.

About You

You will hold a Master’s degree (or equivalent experience) in a relevant quantitative discipline, with a strong background in statistical and computational analysis. You will be confident using coding environments such as R or Python, working with large datasets, and applying quantitative methods, including machine learning and regression‑based statistics, within a reproducible workflow. Experience with AI methods, large‑language models, open data, version control, and data management will be very useful for this role. You will be able to work independently, manage your time effectively, and contribute productively within a multidisciplinary research team.

Thriving at the Museum – the way we work

We are proud to work at the Museum and have identified the qualities we all need to embody to reach our shared ambition. This sits alongside the Museum’s values and forms the framework for the way we work.

What We Offer

• 27.5 days holiday plus 8 bank holidays (full‑time equivalent)
• Generous defined contribution Natural History Museum Pension Scheme (employer contribution 4–10%)
• Season ticket, bicycle and rental loan
• Life insurance
• Free admission to our exhibitions and many other paid exhibitions at museums, galleries and institutions across London and the UK
• Staff discount at our Museum shops and cafes
• Wide variety of training initiatives and opportunities to build skills; we invest in staff development and ambition to help staff grow and fulfil their potential
• Affordable membership to the Civil Service Sports Council, offering a range of benefits including special offers and reduced entry fees at cinema chains, theme parks, theatres, retailers and supermarkets, plus entry to up to 300 English Heritage sites and other national treasures (https://www.cssc.co.uk)
• Membership to our Sports and Social Association (for a small fee), including access to our in‑house gym and clubs such as football, softball, table tennis, tennis and classes in Middle Eastern dance, yoga, and Tai Chi

Hybrid Working

We operate a hybrid working model that requires regular, weekly attendance for this role, with the precise pattern of days on site and worked from home to be agreed with your manager.

How to Apply

If this sounds like you, please apply by clicking on Apply for job. Please note that as part of our commitment to anonymised shortlisting, panels do not view CVs during the recruitment process. If you choose to upload your CV, our system will automatically pull information from your CV into our application form. We advise you to double‑check your application form data before submitting as the tool may interpret CVs differently.

Closing date: 23:59 on 11 January 2026
Interviews expected: week commencing 19 January 2026
This role does not qualify for Museum sponsorship, so the successful postholder will need to have a valid right to work in the UK at the point of offer.

Seniority Level

Entry level

Employment Type

Full‑time

Job Function

Research, Analyst, and Information Technology

Industries

Museums, Historical Sites, and Zoos