Faculty Fellowship Programme - Data Science

Why Faculty?

We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human‑centric AI. You can read about our real‑world impact here.

We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.

Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.

AI is an epoch‑defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.

Artificial Intelligence is the most important technology of our age, but it is only valuable when it is applied in the real world - enhancing products, improving services, and saving lives. Since 2014, Faculty has helped 500+ PhD graduates, post‑doctoral researchers, masters and experienced software engineers transition into a career in data science through our fellowship programme.

The Faculty Fellowship programme helps academics become highly‑skilled data scientists and machine learning engineers, transitioning successfully into careers and industries that are ready to benefit from artificial intelligence. After two weeks of intensive lectures and workshops at Faculty, wherein fellows will learn how to apply their technical knowledge towards the application of data science, fellows are paired with project companies for a seven‑week data science project, during which fellows are paid the London Living Wage.

The finale of the fellowship is Demo Day. Fellows have the opportunity to present their hard work to an audience of 100+ guests. The event is a great chance to network with hiring managers and influential individuals from a wide range of businesses from London, the UK and Europe.

After completing the Faculty Fellowship, our alumni have gone on to work for many leading companies; from tech giants like Google, Microsoft and Meta, to the fastest growing startups like Physics

X and Orbital Witness, to established FTSE 100 companies such as AstraZeneca and Tesco to name a few.

Right to work: You must have the right to work full‑time in the UK. Unfortunately, we cannot sponsor this role
, so please only apply if you have the right to work in the UK full‑time. If this is on the basis of a visa, please provide details of your situation in your application. Visit  to find out more.

Location & logistics: You must be able to work in‑person in London (UK) for the full duration of the nine week programme. Please note that Faculty does not provide accommodation or travel expenses to attend the programme.

In general, successful candidates meet the following criteria:

• A finished PhD or Master’s in a STEM subject with some data science/machine learning experience.
• A high level of mathematical competence.
• The ability to code or have programming experience, especially in Python.
• Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation).
• Experience with some coding libraries frequently used in data science.
• The ability to communicate effectively.
• Experience composing and following a project plan/sticking to self‑imposed deadlines.

• Please submit your application no later than Wednesday 4th March 2026 at 17:00 (GMT). Early submissions have a significant advantage as we have more time to review them.
• The coding test will take place in early March 2026
  . Candidates will be notified about the coding test via email.
  • Following this, we are piloting a short video‑based communication task for some candidates. This is used to refine our selection data and help you demonstrate the communication skills needed to be successful in the programme.…