Faculty Fellowship Programme: Data Science and Machine Learning (Feb 2025)

Please submit your application no later than Monday 28 October 2024 at 17:00 (GMT)

Faculty transforms organisational performance through safe, impactful and human-led AI.

We are Europe’s leading applied AI company, and saw its potential a decade ago - long before the current hype cycle.

We were founded in 2014 with our Fellowship programme, training academics to become commercial data scientists.

Today, we provide over 300 global customers with industry-leading software, and bespoke AI consultancy for retail, healthcare, energy, and governmental organisations, as well as our award winning Fellowship.

Our expertise and safety credentials are such that OpenAI asked us to be their first technical partner, helping customers deploy cutting-edge generative AI safely.

Our high-impact work has saved lives through forecasting NHS demand during covid, produced green energy by routing boats towards the wind, slashed marketing spend by predicting customer spending habits, and kept children safe online.

AI is an epoch-defining technology. We want people to join us who can help our customers reap its enormous benefits safely.

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 400+ 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

You must have the right to work full-time in the UK. Visit www.gov.uk to find out more. 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.

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 Monday 28 October 2024 at 17:00 (GMT). Early submissions have a significant advantage as we have more time to review them.
• The coding test will take place at the end of October/ beginning of November 2024. Candidates will be notified about the coding test via email.
• The interviews will take place from early November 2024. We will interview candidates via Google Hangouts. Candidates will be notified about the interviews via email.
• Regardless of the application outcome, you will be notified no later than the end of December 2024.
• The programme runs from Monday 3rd February to Friday 4th April 2025 (full-time, Monday to Friday).
• Please note you will need to be able to work in London for the duration of the programme. Unfortunately, Faculty does not provide accommodation.

Fellowship Benefits

Fellows are paid London Living Wage for the duration of the programme (ie. the full nine weeks). The two weeks of training at the beginning of the fellowship are free of charge to you.

After the programme, the fellows are welcome to join our free online community where all the fellowship alumni discuss data science news, techniques, conferences and much more. Faculty provides career support to the graduated fellows - once you are part of our "professional network", we will be delighted to keep in touch and help you succeed in your data science career.

What we can offer you:

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

Faculty is the professional challenge of a lifetime. You’ll be surrounded by an impressive group of brilliant minds working to achieve our collective goals. Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you’ll learn something new from everyone you meet. You’ll also have the opportunity to make your mark on a high-growth start-up now poised to expand internationally.

We are proud supporters of Women in Data®.  Connect, engage and belong to the largest free female data community in the UK – visit: www.womenindata.co.uk to join our community.

APPLY HERE: Faculty Fellowship Programme: Data Science and Machine Learning (Feb 2025)