Artificial Intelligence and Computer Science student in the finals of Future CFO of the Year competition
Alistair Tait, 1st-year student of Artificial Intelligence and Computer Science placed in the final of Future CFO of the Year category of the TARGETjobs Undergraduate of the Year Awards.
Alistair’s dream is to found his own start-up. The skills he gained in his course in the School of Informatics combined with the business and entrepreneurial skills he hopes to develop further will be a head start. He decided to take part in the competition on a whim. He saw the competition advertised and thought there was no harm in giving it a go so his success comes as a bit of a surprise.
Reaching the final in the CFO was very exciting. It was a fantastic opportunity to showcase his passion. The competition provided him with great experience, especially since he was competing against many older, more experienced candidates.
The annual competition aimed at finding the best undergraduates in the UK. Each award is partnered by a prominent graduate recruiter who provides a fantastic prize for the winner, including a paid internship, trips abroad and other exclusive opportunities.
The Future CFO category is partnered by the Chartered Institute of Management Accountants and the organisers are looking for business and finance savvy students who possess a variety of skills and personal qualities that will help them become a future business leader such as a commercial mind and entrepreneurial spirit, an interest in how finance adds value to a business, good ethics, integrity and morals, high aspirations and the desire to make an impact on your life and others, creativity, communication skills and a high level of digital literacy and a growth mindset.
Showcasing my passion was great fun and a great opportunity at the same time. I would also like to wish a big congratulations to Vanessa Lee, the other University of Edinburgh finalist who won her category for the undergraduate of the year for sustainable thinking.
MSc in Advanced Technologies for Financial Computing