The Career Voyager & The Power of Communication

"Think of a data scientist as a professional at using data. Any data." 📊

Coming from an Economics background, Erin didn’t always think she’d fit the mold of a traditional scientist, especially since she started with zero programming skills! But her journey at Naimuri has shown her that the tech world is far more diverse and social than she ever imagined.

In her latest Q&A, she talks about:

• The puzzle of incorrect classification models.
• The communication superpower: Why translating technical jargon into plain English is the most important tool in her kit.
• Smashing myths: Why data science isn't a man’s world, and why your undergraduate degree doesn't define your destination.

To anyone considering a pivot into tech: be inquisitive and try everything. There is no one-size-fits-all path!

Check out the full conversation with Erin here: 

How would you describe your role at Naimuri to someone who has never heard of Data Science before?

Think of a data scientist as a professional at using data. Any data. Every time you use an app, buy groceries, or stream a movie, data is created. On its own, this data is just a massive pile of messy numbers. A data scientist uses tools like maths, statistics, and computer programming to find the story hidden inside that pile, and use it to the benefit of our customers.

Our role generally follows three steps:

• Investigation: Ask big questions, like, "Why are we seeing this trend?” or, “How do we make that technology work better, whether that be by personalisation or accuracy?”.
• Discovery: Build "models" (digital blueprints) that use the data to provide insights or predictions. This could be predicting the spread of the coronavirus, or training a self-driving vehicle to identify the large object in front of it as a lorry or a tractor.
• Strategy: How do we use the insights provided? How can we trust the model output?

Essentially, we turn raw data into predictions. If a streaming service knows exactly which show you’ll want to watch next, or a bank spots a fraudulent charge before you do, a data scientist is likely behind the scenes making it happen.

Walk us through a typical day. What are the specific 'puzzles' or challenges you are usually trying to solve?

My days vary tremendously. I could spend an entire day trying to work out why my model is 85% confident that my image of a pigeon is a swan, for example. Then the next day I’m translating our statistical techniques to plain English so the customers understand it, as well as researching new model evaluation techniques, or even writing for this blog post.

Generally speaking, a day in the life of a data scientist follows the pattern of:

1. Log on, participate in a team meeting to discuss project progress or any blockers.
2. Evaluate a model, is it doing a good job (classifying a pigeon as a pigeon)?
3. Research (what new techniques could I use to make my model better?).
4. Lunch (Wordle is non-negotiable here!).
5. Fine tuning my model (making it more accurate).
6. Writing the report (what worked, what didn’t, why?).

What is one soft skill that you use more than people would expect in a technical role?

Communication! Data science has some complex explanations, and you need to be able to translate all of that technical jargon into plain English.

Was there a specific teacher, mentor, or moment that shifted your perspective toward science?

I’ve always had a rather mathematical brain, it was my favourite subject in school because it was entirely logical. 

After completing my undergraduate degree in Economics, I thought about pursuing Data Analysis, but I had a family member who worked in the tech space and showed me that Data Science was just more interesting.

Following the AI boom due to the release of chatbots like ChatGPT to the open market, it was a no-brainer. I took up a Master’s in Data Science and haven’t looked back.

What did you study at University, and how did those studies prepare you for the real-world data challenges you face now?

I did an MSc in Data Science. All in all, it set me up with the foundations of Data Science and how to think like a scientist. However, the majority of my learning has been at work. The actual application of data science components is completely different in reality compared to academia.

Did you ever feel like you didn't "fit the mold" of a traditional scientist? If so, how did you overcome that?

I didn’t think I fit the mold of what I had assumed a data scientist to look like, for one because I had zero programming skills before starting my degree, and for another because I have always been a people person.

I had thought that working in tech would mean sitting on your laptop in a room by yourself. My Master’s degree got me over that quickly. I met a whole variety of people from all different backgrounds with one of my friends having an undergraduate degree in Fashion, another in Psychology, and the majority of my cohort were bubbly and chatty personalities.

We worked together to solve problems and share our interests, and it showed me that I could be a successful data scientist and not lose my sociability.

For those considering a Science background, do you think the career opportunities are as broad as people say?

Absolutely, they are incredibly broad.

What is the biggest misconception about being a woman in Data Science that you’d love to set straight?

The biggest myth is that data science is a man’s world. It isn’t. Our team at Naimuri has a female majority, and there was nearly an even distribution of females to males on my course at university. It was even a female who won the award for being the top of the year in our class! You can be a power house in the world of data science as a female just as well as your male counterpart.

What needs to change in the wider world (beyond just school) to encourage and retain more female talent in tech?

For some reason, there’s an underlying perception of it being a bigger deal for a female to go into the tech space. I have had male friends that went down the development route and when talking about work, people would nod and it was almost expected. On the other hand, their female counterparts would get an, ‘oh wow how clever, look at you!’, response. I think this exacerbates the social bias that these roles are male, and that it’s a greater undertaking for a female.

If you could go back to your 15/16-year-old self when she was picking her subjects, what would you tell her?

I'd probably tell her not to stress about it so much to be honest. Yes, being well rounded in your subject choice is useful, but not every subject has to be what you assume looks good on your university application. Having good grades in subjects you like and thrive in are more important than sub-par in what you aren’t actually interested in.

Also, university is not the only path! I know plenty of people who went down the university route because they thought it guaranteed a job when it didn’t, and plenty of others who took the apprenticeship route and were more secure. There is no one-size-fits-all when it comes to your career path; be inquisitive, try everything you can, and if it doesn’t work, you can always pivot.

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