We know that we’re only as good as our people, and at Ada Mode we have an impressive team at the helm. This Humans Behind the AI series aims to highlight the talented individuals that work here. For this month’s edition, we’re featuring Nick Barton, Senior Data Scientist at Ada Mode.
Senior Data Scientist.
The average day starts with a meeting with the rest of the data science team, discussing our focus for the day and working through any potential blockers or challenges. Then I’ll spend a good chunk of the day programming, building on AWS, exploring some data or training ML models.
Later in the afternoon I’ll probably take a break and chip in with some marketing or business development work. This might involve writing up a recent experiment as content for the website or helping to define new solutions to identified industrial problems.
I get a lot of time to be creative and to experiment with new technology which I definitely value. It’s also great working closely with other data scientists to build exciting products and services in collaboration with engineers and domain experts. The variety is nice, which comes naturally at a start-up.
Probably the pilot study we did for the application of computer vision to the rail industry. It was one of the first things I did at Ada Mode and it was a challenge to work with techniques, libraries and a data type that were new to me at the time. It was satisfying to achieve the results we did, taking the project from an identified problem to a demo solution over a very short development cycle.
I’m more passionate about the potential solutions that machine learning can provide rather than I am about the actual technology and algorithms. Particularly with application to renewables, the operational benefits and value that ML is and can bring to the industry is promising and I’m passionate about sharing that capability and bringing it to new sectors.
I think it will become easier to deploy machine learning solutions, it presents its own challenges in the software development space and the tooling to enable effective deployment isn’t as well established as it is in more typical software. This should lower the barrier for entry and enable more widespread adoption of ML solutions across multiple sectors.
Work: I built a bot which reviews our company action trackers and other logs and sends automated reminders and update emails to the team. It links in with our Microsoft cloud storage, so it was a good chance to me to get more to grips with some Azure services.
Not work: How to play random songs on the guitar.
At the moment:
Recently, I enjoy discovering new music, learning new things and watching too much TV! But in more normal times, I play a lot of football, tennis and golf.
Keen to find out more? Click here to learn more about our team.