Data science novice to AI artisan: A fellow’s journey with Faculty

Like many other fellowship alumni, I arrived at Faculty slightly nervous, but mostly excited to be starting training for a new chapter of my career. I was optimistic that data science might be the more attractive sibling to academia, despite having gained much from it.

2019-04-03Fellowship

Like many other fellowship alumni, I arrived at Faculty slightly nervous, but mostly excited to be starting training for a new chapter of my career. I was optimistic that data science might be the more attractive sibling to academia, despite having gained much from it.

I knew that what I found most rewarding from my PhD was the process of finding solutions to complex challenges, and communicating challenging ideas by presenting them in believable and compelling ways, backed up by data. But after four years, I was beginning to feel that my tenacious pursuit of new ways to cool atoms and molecules to near-absolute-zero using laser light was becoming a bit of a crusade. It lacked variety and was difficult to justify at dinner parties. Thankfully, I had a plan. I had previously witnessed two colleagues successfully transform themselves into productive members of society through Faculty’s fellowship programme, and was curious to find out more.

Finding a host company and building data science skills

Fast-forward three months. Equipped with what little experience I had hastily accumulated through Kaggle competitions and the obligatory MNIST neural-network tutorials, I arrived at Faculty HQ. Almost immediately the process of pairing fellows and host companies began. The fellowship can feel intense at times, but there is always a reassuring sense of camaraderie and teamsmanship.

By the end of the first week I had come to know 20 other fellows, digested pitches from a range of exciting potential host companies, come to a decision on which projects appealed to me the most, and thrown myself into the curriculum of soft-skills and technical-skills workshops. What follows is a second week of technical training, during which fellows build up a repertoire of skills through hands-on experience with the latest data science and machine learning (ML) techniques. Then, after two short weeks, the placements begin.

Placement: Putting it into practice

The six-week placement is the core component of the fellowship programme, and a fantastic opportunity to apply the technical skills acquired through the fellowship curriculum to real-world business problems.

For the duration of the placement, fellows meet at Faculty each Friday. Some of the more advanced technical workshops are reserved for these days, but the highlight for me was finding out more about everyone’s experiences: what techniques people were using, what it was like to work with their host company, and what challenges they were solving.

In a group of 20 unique projects it is inevitable that some tricky situations arise. As a fellow you are extremely well supported, in constant contact with both technical and commercial mentors, other fellows and one very charismatic cohort leader. Challenges were approached as learning opportunities and always resolved.

Demo day: Sharing the outcomes, communicating the impact

Throughout the fellowship there is an emphasis on the final deliverable: our Demo Day presentations. The five minutes of fame when you are given the invaluable opportunity to showcase your work to a room full of excited data scientists and hiring companies.

Preparations for Demo Day are exhausting, but the results speak for themselves. Each presentation was engaging, looked the part and was packed full of ML-powered algorithms primed to optimise, automate or disrupt the status quo.

Afterwards, there is a real sense of achievement and pride across the group. I suppose that most people who attend Demo Day are predisposed to believe that we might be on to something with our projects, but their genuine enthusiasm really makes you feel that you have the potential to become a competent and impactful data scientist.

There then remained one last thing to do (actually, two, because you have to write a final report). Powered by an unstable ratio of drinks to canapés (a solitary quail’s egg), I set out to celebrate with my fellow fellows, revelling in the knowledge that I wouldn’t need to flounder for professional justification at dinner parties again.

If you’re interested in applying your skills in the real world, you can apply to our 16th fellowship in May here.