Kyle Mcenery

I'm Kyle, I'm a graduate data scientist and I work at a boutique consultancy that specialises in healthcare, and most of my work focuses on healthcare analytics, just working with data to understand systems, outcomes and decision making.

I spend most of my time wrangling with healthcare data to try and help in various consulting projects. So it often involves just cleaning, structuring datasets, analysing trends and patterns, and trying to pull out some insights that decision makers can actually use.

A typical day, well, I usually get up, I have two coffees, which is imperative before starting work.

Then I usually review what's called my Jira board. It's like a little task sheet of things that I have to do, all my responsibilities.

A basic day, sometimes we can start with an analytics scrum. So the different data scientists in the company will get together, we'll collaborate, talk about any problems we've run into, help each other on tasks.

And then I pretty much schedule all of my work in between different meetings. Obviously in consultancy you work between different projects so you can have daily meetings for each project to make sure the team's aligned and you all know what you're doing and what you need to be doing.

So in between that I'm pretty meticulous, so I'll schedule, okay I need to work for this project between those hours and then those for others. You do get that bit of independent aspect, you know, to managing your own work.

Well, who I work with changes from project to project, but in terms of like a basic outline, I will work with what's called a consultant.

It's someone who mainly manages communication, relationships, and helping bridge the gap between all the analytics work I do and the clients we actually work with, because they're generally more non-technical audiences.

So I can spit out a bunch of numbers and figures to them. And if I was doing it, they may not understand it. So they're basically needed to do the translating of complex ideas into something clear and useful, something people can actually make decisions with.

On more analytics heavy projects, I'll also work with a senior data scientist and they'll usually give me like technical support and collaboration on the more maths-y and analytics stuff.

Like in a recent project, I was building a dashboard and my managing data scientist, he was helping me build it all the way through. I had the main sort of delivery aspect of it, so I was coding it and building it, but he would sort of give me guidance when I needed it.

The best thing about my job? I think this comes down to my personality type, but the best thing about it is the variety of problems I get to work on and the variety of projects.

I mean, I work best when there's novelty and different challenges to solve. Like I would get very stuck in the mud and very bored if I was just doing the same thing over and over again.

Like I have friends who work in software development and their job is pretty much the same day in and day out. It just never changes.

But even if the workday looks similar on paper for me, I'm solving a great variety of problems, like one day to the next, the work just varies massively.

So for example, I told you I was building a dashboard at one point, but a couple of months ago I was building a massive machine learning project to sort of help different clinicians make good decisions for patients.

And yeah, that involved me doing a lot of travelling, a lot more complex coding than you would do on something like a dashboard.

But there's different elements to each project you work on. So one can be more creative, one can be more numbers and analytics focused.

But that's what I find particularly fulfilling about my work.

The hardest part, and this is specific to consulting as well if you're working as a data scientist in this industry, and that's that you have to balance structure and adaptability.

People in analytics, the things we build, we like to build them in a very meticulous, very sensible and logical way. But as you're working through a project, the things that a client or your colleagues are asking for are always shifting.

So you really have to balance keeping your work very well organised, but also just making these little changes here and there. And it can be even little things like, we want to see this graph on a yearly basis instead of a monthly basis. Or maybe they'll take a more large detour, but you have to be able to adapt that way.

And it is challenging, staying flexible and adapting quickly. But I think it builds resilience, which is no matter what industry you go in, you're going to need that.

So my background is actually in theoretical physics. I completed my Master's in theoretical physics in 2023.

And eventually my friend actually landed a job at this company. So he was a data scientist the year prior to me, and he actually recommended me.

It was kind of a stroke of luck, to be honest. Like, you don't meet a lot of data scientists or aspiring ones that have like an interest in healthcare. But for me, I actually considered studying medicine before I went into physics.

So all of my work experience was just like shadowing doctors in like cardiology, neuroscience. So I got a good idea about healthcare and how it worked beforehand.

So they saw that on my CV and they were like, you know, it seems like a good fit.

It's funny actually, I think my education helped me massively, especially with my physics background.

I mean, you often hear people joke that they never used the maths they learned in school. You know, it's very cliche, why am I learning all this? I'm never going to use it. But I'm in the fortunate position where I use it all the time.

And I actually originally studied physics because I wanted to understand the universe just fundamentally. I wanted to go from the smallest scale to the biggest picture. And it just so happens that maths is really the only language that makes that possible.

So a lot of my ways of thinking very naturally sort of tended towards analytics and data science. I actually only started coding and doing programming in my undergraduate degree and as you can imagine like I was terrible at it at first.

There's a very steep learning curve when you start, that's one thing I would warn people. But it's kind of like learning a language so if you you know what it's like learning a language for the first time it can be quite tricky but I think it was very helpful that I learned in an academic environment, because it gave me a strong foundation in using programming and coding.

Specifically for a given project and something that was expected of me, like in terms of assessments. I mean, you know we have like AI tools and, you know, they're ruling programming and coding, but I think it's good to have a foundation of actually learning a programming language first and then learning how to, you know, use the AI as a tool layer.

Obviously, there are a lot of different areas you can work in as a data scientist, not just healthcare. You can go more into the finance side, for example.

But with my job specifically in being in healthcare analytics, I think it benefits people in terms of you're not just sitting around making numbers go up on a screen to make people profit. There's a real human impact to the work we do at my company.

So even if it's just working with clinicians to help manage their day-to-day operations and help them. But it trickles all the way down to patients and the experience they're getting with healthcare systems.

I mean, we know about like all the challenges the NHS has gone through and I feel day to day that my work is actually helping push the country in the right direction or at least I like to hope so. Yeah, I'd say the human impact.

Well, one interesting thing about working with my company is you get to travel a lot. We have different projects all around the country.

And I'd say the one that stands out to me was when I traveled to Ireland for the first time, because actually most of my family's Irish and I'd never had the opportunity to visit before. So it was quite surreal, you know, being in that airport and realising it.

I mean, it also helped that the project that I was doing was possibly my favourite project because the people I was collaborating with, they were very supportive and they were helping me. Like it was a very difficult project in terms of what we were delivering, but I had all the support I needed through the way.

And I was just sort of sat there in the middle of this, well, different country and just couldn't really believe I was there and that it was work that had brought me there.

Honestly, I think I'd tell myself to slow down a bit and be more present.

I was always like very focused on the future, the next milestone, always thinking about where I needed to get to next, you know, not even just academically, but everything I did, I was very goal orientated and had that tunnel vision for the future.

But over time, I've realised it's important to actually just enjoy the process just as much as the destination. Things are going to move quickly and you're going to miss a lot of it if you're only focused on where you're going.

Sometimes you just got to stop and appreciate where you are.