Meet the Tockers


Machine Learning Engineer

Minseok joined Tiimely back in 2018 and was the first member of the Data team. His role has evolved from Data Engineer to Machine Learning Engineer – but today, we learn about what makes Minseok tick

Headshot photo of Machine Learning Engineer, Minseok.

Minseok has been a mainstay member of Tiimely’s Data Science team since 2018. He’s focused, diligent, and sharp as a tack. You’ll typically find him working quietly away on a project, with his head deep in code, refining machine learning algorithms. We spoke with him about his role to get an inside look at Tiimely.

Had you heard of Tiimely before?

It was about three and a half years ago when I joined Tiimely. Back then, I hadn’t heard of Tiimely, well, we were called Tic:Toc then, but things have changed now. When I talk about home loans with my friends, they know us. I now get asked several questions about our rates and products. I’m glad that more people have started noticing us. I’m sure many more will too.

What were your thoughts when you first stepped out of the lift and into the Tiimely office?

Back at our old offices on North Terrace, when I stepped out of the lift, old wooden floorboards and an industrial ceiling greeted me. People were full of energy and cheerful. As I walked down the corridor there were many random items: a paper mache head and a large, life-sized granny cut-out. People seemed to genuinely have fun working at Tiimely.

Is Adelaide your hometown, and what do you enjoy about working in the city?

No, Adelaide is not my hometown since I was born in Seoul, South Korea. But, Adelaide is like my hometown and especially to my family since we’ve spent the majority of our time here. I do enjoy working in the city. Everything is nearby, and it’s great to commute. There are heaps of options for lunch, snacks, and coffee!

What do you find most challenging about your role?

As a machine learning engineer, I feel it’s challenging when I can’t have straightforward answers and fast results. These are natural challenges that all machine learning engineers face. You have to go through a lengthy process starting from data collection to model training. And, there is no guarantee that your approach will be successful after the long process. Think of a detective who has to collect as many pieces of good evidence as possible to make the best guess. In my context, it is data that directly impacts the quality of my analysis and machine learning models. Preparing quality data is a big challenge because it requires a long time and manual work. And, it needs a lot of patience.

There are certain things that money cannot buy, and I think working here is one of them.

- Minseok

What do you find most rewarding about your role?

It is very rewarding when my work leads to a product that improves people’s lives. It is especially rewarding since you invest a long time in developing it. There is that satisfaction when you finally see it bearing fruit.

How would you explain your role in 10 words or less?

A detective who collects evidence and makes the best guess.

What advice would you give to someone wanting to get into a similar role?

I would recommend them to learn back-end coding. The code you use in the machine learning and back-end domains are different. In machine learning, you code to prepare data and train machine learning models using the data. Where does the data come from? From the main product. If you can understand the language that’s used to build the main product (in our case, C#), you can build your data pipelines and implement machine learning models you trained using the data. You can have a wider spectrum in your work.

What are you reading or listening to right now?

I’ve been reading “Salt Sugar Fat: How the Food Giants Hooked Us”. It explains how the U.S. food industry used and is using those three ingredients to attract kids and adults and make them addicted to them. It was interesting to know that they have a math formula that calculates a level of addictiveness.

You win $10 million playing Powerball. Do you keep working?

Yes, I think I’ll keep working. There are certain things that money cannot buy, and I think working here is one of them.

How do you like your coffee?

My first coffee is usually long black. After that, I’ll have another long black or latte.


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