Three times a year, we take top PhD graduates and software engineers through an intense programme of data science and data engineering and how they are applied in industry.
During the Fellowship each Fellow works on a big data project with real business impact at one of the most innovative companies in Europe.
Each cycle culminates in Demo Day, where Fellows will present their project to an audience of the leading data scientists, business leaders and hiring managers in London.
The Fellowship doesn’t end on Demo Day. We have an incredible community of 80+ Alumni working as data scientists and data engineers across the top companies in London and beyond who will be part of your network for life.
The generosity of our industry sponsors means that participating in the Fellowship is free of charge for Fellows.
From academia to Data Science
From Software Engineering to Data Engineering
Impactful and low risk
PhD in Astrophysics, Università La Sapienza
An adaptive learning system for Cambridge University
PhD in Computational Biology, Imperial College
Developing a recommendation engine for TVPlayer
PhD Molecular Cell Biology, UCL
Using NLP for London Fire Brigade to reveal incident types not recorded
PhD in Biomedical Engineering, King's College London
Developing a SherlockML app to show an area's demographic information
PhD in Mechanical Engineering, Imperial College
Forecasting traffic and ridership conditions to improve bus scheduling
PhD Particle Physics, Imperial College
Automating detection of rogue landlords in Greater London Local Authorities
PhD in Astrophysics, Universitat de Barcelona
Building a data quality service for the SherlockML platform
PhD in Mathematics, University of Bristol
Predicting customer retention and factor weighting at online pet food retailer Tails.com
PhD in Mathematics, University of Cambridge
Deep Learning for Weakly Supervised Object Detection in Computer Vision with AI startup Tractable
PhD in Financial Economics, European University Institute
Predicting performance in sales teams for human resources company Hudson
PhD in Theoretical Physics, Imperial College London
Building a recommendations system using machine learning for Nectar card
PhD in Physics, Princeton University
Measuring air quality in parks in London using Open Data
The ASI fellowship was a fantastic opportunity for easyJet to experiment with data science in a low risk manner. We hired the Fellows that were working with us, and they have been the foundation of the data science team at easyJet. This is a unique programme that has greatly benefitted easyJet!
I’ve highly recommended the Fellowship to others. It’s a great way to find very bright Data Scientists and complete well defined Data Science projects.
My experience with the ASI and the Fellowship was fantastic - it helped to understand the power of data science, and set the context for ideas in the area for the next phase of of work. I've continued to work with ASI, in a variety of capacities, and found that their combination of technical expertise and experience with transformation management is unrivalled.
I would recommend the Fellowship, particularly to companies that want to get into data science but don’t have existing teams. Hiring data scientists is very difficult due to the churn in the market and the novelty of the technology, and ASI does a lot to help along that journey.
I would definitely recommend the Fellowship. It's hard to find great talent and the Fellowship is very helpful for hiring - every Fellow you've selected was worth meeting with.
Being on the Fellowship pushed us out of our comfort zone and let us learn more about data science and its potential value to us. I have already recommended it to others.
I would absolutely recommend the Fellowship to others. In fact, I already have. If you have some data and an idea that is just out of your reach, this is the programme for you.
The Fellowship was very well organised and run, every interaction we had with you, your team or the Fellow was positive and we are very impressed with your company and the Fellowship. I would absolutely recommend.