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 .
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.
About the Fellowship
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 PhysicsX and Orbital Witness, to established FTSE 100 companies such as AstraZeneca and Tesco to name a few.
Requirements
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.
Timelines
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.
Regardless of the application outcome, you will be notified by Friday 17 April 2026 the latest.
The programme runs from Friday 15th May to Friday 17th July 2026 (full-time, Monday to Friday)*
*Please note that the program dates are tentative and may be adjusted or postponed entirely. We will notify all applicants immediately should any changes occur to the dates or the program schedule. We appreciate your flexibility and understanding.
Fellowship benefits
Paid experience: Fellows are paid the London Living Wage for the full 9-week duration of the programme.
Intensive training: Two weeks of technical and commercial lectures and workshops focused on applying data science to real-world business problems. Then continued training every Friday throughout the programme to ensure you have the full foundational grounds required to successfully transition into permanent data science roles after the fellowship.
High-impact projects: Seven weeks embedded with a project company, working on a real data science project.
Career transition: Proven track record of helping academics move into data science roles at various companies and industries.
Professional network: Access to a network of 500+ alumni and the opportunity to present your work to 100+ industry leaders at our 'Demo Day'.
Our Recruitment Ethos
We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.
Some of our standout benefits:
Unlimited Annual Leave Policy
Private healthcare and dental
Enhanced parental leave
Family-Friendly Flexibility & Flexible working
Sanctus Coaching
Hybrid Working (2 days in our Old Street office, London)
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please do apply or reach out to our Talent Acquisition team for a confidential chat - Please know we are open to conversations about part-time roles or condensed hours.
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