Why did you choose FMC Data Solutions?
At FMC Data Solutions we have a great opportunity to work on products that will help people. In the end, the work directly relates to improving patient healthcare outcomes and this was a major draw for me. There was also the potential to work on projects covering a wide range of skillsets and domains across data science. I knew it wouldn’t be boring! Finally, I was impressed with the existing work that had been done to establish the team within the wider organisation and the high level of professionalism on display during the interview process.
What do you particularly like about your work?
Working as a data scientist feels a little bit like being a detective. We follow evidence, explore data and discover new things. The process is very satisfying in itself. The job also provides an opportunity to work on all areas of the product lifecycle, from project definition, experimentation and prototyping, to deployment and scaling. From the perspective of a team lead, I really enjoy working with others to develop their skillsets and careers.
Where do you see the biggest revolutions in the AI and Medical Devices sector?
In data science there seems to have been a recent shift towards explainable and interpretable machine learning models. On a related note, causal inference also seems to be becoming more popular within the machine learning community. This trend is particularly applicable to the medical devices sector, where explainability is at a premium. It’s also hard to go past the recent advances in attention based models. While most relevant in natural language processing, I don’t believe it will take too long for these methods to filter into the work we do.
What does it take to make a Data Science team productive?
First and foremost, team members should have the right skillset. An understanding of the fundamentals of statistics, machine learning and programming is essential, and having a diversity of specialisations is a nice bonus. In addition to technical skills, the ability to communicate well is vital. Beyond that, having a nice mix of characters and personalities is great for team atmosphere. It’s very rare that someone is an expert in all fields of Data Science, so it’s important to foster an environment where everyone is able to ask questions, learn from others and that there is little fear of failure.
What is important to you when developing AI talents?
We’re looking for people that are open, honest and curious. People that have a willingness to learn, a passion for the field and a bit of persistence will do well in the long run.