Project volunteer Patrick Savoie
“After my first DataDive project, I knew this was an organisation I wanted to be involved with long term - it was a great experience all around.”
Pronouns He/him
Roles Product Data Scientist at AccuRx
DataKind UK Data Ambassador
Links LinkedIn
Patrick hails from Canada and has always wanted to apply his different skills to helping underserved communities, and use data science approaches to tackle social problems. Thankfully, he found the DataKind community, where he fits right in!
How did you first get into data?
There’s a lot to this question! It was never really in my plans, it was a gradual evolution. During my undergraduate degree, I did summer research in biomedical engineering. We worked with a mixture of biomedical and machine learning (ML) techniques to improve clinical tasks. I really enjoyed it, but I didn’t know if you could make a career out of that research. The other thing that prompted me to get into data was that I was always interested in using my skillset to find out how to help underserved communities, or for social good in general, but I wasn’t sure how to go about it.
I got involved with Engineers Without Borders in Canada (EWB), which was a really good way to figure out how to direct my energies. At the end of my undergrad, there was a position to work as a data scientist, as a long-term fellow with EWB in Uganda. Instead of having you build a well, they placed you at a start-up to build a role, and then train someone local to do that role. I was placed with a fintech startup, and as the only data scientist the role was varied: data analysis, SQL queries, and building up their data infrastructure. There was some data science — it was great to see how to do that in a business setting — and my goal was to replace myself by the end of the fellowship. I’m really happy I had the opportunity to do that. It was a cool experience that tied together my love for research and supporting underserved populations. It came at the perfect time and set me up to be a data professional.
Now I’m a data scientist for a health tech startup called AccuRx — we work with the NHS to improve communication. For instance, we’re encouraging people to make more use of the NHS app. My role is a mixture of things, but more specialised in causal influence and ML, using a lot of user feedback and topic modelling.
What do you wish you’d known when you started to get into data?
There are a lot of different ways to be involved in data — it’s a very broad field with a huge range of skill sets. That makes it very difficult to get your first role, because there are a lot of different types of data scientists. Some will be on the analysis side looking at business intelligence; some in causal inference doing experimentation or running models to figure out cause and effect; then there are ML engineers; and a lot of roles anywhere in between.
The advice that I have is to focus on a niche, because it can be overwhelming to focus on multiple areas at once, especially at interviews. And all of the areas change over time! When you get your first role, try to hone in on what you’re passionate about, or most interested in. Then as you progress through your career, you become specialised in one area.
My second piece of advice is more difficult to realise until later in your data science journey - get mentorship. Unless you’re at a huge company, you’re often one of only a few data scientists, or sometimes the only one. That means you have to figure out a lot of stuff on your own, and as a relatively new role, ‘data scientist’ is not that well-defined. That makes it very difficult to know if you’re going in the right direction, and which skills to develop career-wise. So look for external mentorship or communities like DataKind whenever you can. DataKind is a great community because you meet so many other data professionals.
What is a data project that inspires you?
One of the data projects that inspires me most (and got me into data to begin with) is a project at the biomedical institute at my undergraduate university. They’re working on ways to improve rehabilitation for people with mobility issues using sensor data and machine learning — I find it so cool that this is possible! I know quite a few people with mobility issues — and with an ageing global population, I think these kinds of projects are a way to substantially improve people’s quality of life, and give them their independence back.
What have you done so far with DataKind UK?
Working with electrical recycling charity Material Focus was my first DataDive project as a Data Ambassador. That was a nerve-wracking, but exciting experience. I took more of a project management role, making sure that everything was going well for us and for Material Focus too, making sure we answered all the right questions. It was a great way to be introduced to the DataKind community. It felt like we were figuring things out as we went, but it was amazing seeing the results of all of our work. I hope that it’s the first of many because it was a really great experience!
What drew you to volunteer with DataKind UK?
What drew me to DataKind was hearing other people’s really positive experiences with the organisation. I was looking for ways to both meet other friendly people in data, and work towards having social impact with my skill set, so DataKind checked both those boxes. After my first DataDive project, I knew this was an organisation I wanted to be involved with long term — it was a great experience all around.
What surprised you most about volunteering?
I was new, I didn’t know what to expect! What amazed me was the varied experiences of people in the data science community — it’s always amazing seeing everyone’s unique journeys into data science. People come into DataKind from different spaces, and fields, with a variety of skills. It really reflects the variety of work you can do in data. And everyone was so down to earth and chill — it was an amazing experience meeting them.
Is there a resource you’d recommend to the community?
One of my favourite books, related to the effects of data science on the world, is Weapons of Math Destruction by Cathy O’Neil. It’s a really important read for people in data to consider the ethics of their work, and the potential negative impact that data science can have on the world if it’s not used for social good.
I think that ethics should be baked into the work of every data scientist so that data is used to make a more prosperous and equitable world, rather than only considering the profits it can bring to companies.
Tell us something completely non-data-related about yourself!
I really love nature, being from Canada, and it’s harder to come by in the UK. I love going on camping trips. I recently went on a cycling trip to Sweden with some friends — at one point we rode as a group of 12 people for about 11 hours!
I also love to play acoustic guitar, and generally love music as a tool to bring people together. Coming from a small province in Canada, I like bringing that wherever I go. I also love dancing — salsa, merengue, swing — anything that unites people!
