Our Fellowship programme connects organisations with STEM PhD, master’s graduates and postdoctoral researchers transitioning from academia to data science careers.
Over the past nine years, we’ve had over 250 host companies participate and with three cohorts each year, the programme has provided more than 400 academics from across the UK and Europe with the skills needed to progress into data science jobs. On top of that, the programme has been recognised as an outstanding training and skills development programme by the Princess Royal Training Awards standard of excellence.
This month we are celebrating our 25th Fellowship. That’s why we’ve gathered some impactful data driven projects we’ve seen over the years that were delivered by our exceptional fellows.
WWF – Understanding the UK public’s relationship with food & diet
The World Wide Fund for Nature (WWF) is the world’s leading independent conservation non-profit. Previous fellow Ned, worked with the campaigns team to explore the UK’s relationship with food and diet using their survey dataset. Applying machine learning models to segment audiences, this project aimed to create better informed communications, support future policy work and assist eventual campaigns to mobilise these groups. See the full presentation here.
Rolls-Royce Motor Cars – Forecasting service retention
Rolls-Royce Motor Cars is a global manufacturer of luxury automobiles. Paired with fellow Jamie, the aim of this project was to forecast service retention of customers using a machine learning pipeline. The algorithm created quarterly predictions to foresee when customers will come in for servicing and their reasons for doing so. The outcome of this enabled dealers to better accommodate their customers and ultimately, increase revenue. See the full presentation here.
BBC – Predicting article performance on social media
The BBC (British Broadcasting Corporation) is the world’s leading public service broadcaster. Our previous Fellow Anna worked on solving an acute business problem; Building a model with the aim of predicting engagement metrics on various social media platforms for unseen articles. With the help of her previous experience as a Data Analyst, the impact of this project demonstrated a highly accurate way of providing unique performance insights and bringing data closer to the newsroom. See the full presentation here.
Yulife – Identifying types of app users
Yulife is an innovative life insurance startup, whose app uses gamification and peer-to-peer encouragement to promote healthy activities such as exercise and meditation. Partnered with Jonathan, the aim of the project focussed on using dimensionality reduction techniques and clustering algorithms to identify types of app users. With a deepened understanding of the motivations of those using the app, the results facilitated a proposal of targeted in-app messaging to drive and enhance user engagement. See the full presentation here.
Otta – Improving accuracy and targeting of job postings
Otta is a startup with a candidate-first search platform revolutionising job hunting within the tech industry. Working with Júlia, the aim of this project was to provide the best job recommendations to users. This was undertaken by using large, structured datasets to apply collaborative filtering algorithms and neural networks. This resulted in improved accuracy and targeting of personalised job postings. See the full presentation here.
Please visit our YouTube channel to see many more video case studies here.
See what major companies are doing with AI
Get insight into what innovative companies are doing with artificial intelligence and see the current cohort’s fellows present their initiatives at this month’s Demo Day. Register here!