From Neuroscience to Data Science & AI: Navigating Rei Masuda’s Career Transition

From Neuroscience to Data Science & AI Navigating Rei Masuda’s Career Transition

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Rei Masuda is always learning.

Through the process of gaining a PhD in neuroscience, he became interested in the opportunities and benefits of data science and artificial intelligence (AI) and how they can be applied to his other passion: mental health.

After some educational experiences in that space, Rei switched to data science, determined to maximize mental health applications and positively impact people’s lives.

We caught up with Rei recently, keen to hear about his fascinating journey into data science and to discuss his experience with the Institute of Data’s Data Science & AI Program.

Read on to learn how the program helped him secure his current role and the advice he has for those considering a career in this ever-expanding field.

1. Rei, please tell me a bit about yourself.

I am an Australian through and through. I was raised and spent most of my life here, but I’ve always longed to connect with my Japanese heritage.

Some years ago, I got the opportunity to work in Tokyo for about a year, and that really got me in touch with my roots. After that, I discovered I don’t have to choose between my cultures and that I can be both.

My fascination with both my cultures shaped my interests in a lot of ways.

I grew up in Perth and I was always outdoors. I love fishing and sports, so I feel so lucky to have grown up where I did.

Going to Japan got me into food and cooking, and I became obsessed with Japanese food and its culture and history.

Australia is a country built on immigration, diversity, and multiculturalism, so for the most part, it’s quite easy to feel comfortable.

Noticing the differences between Australia and Japan has been enlightening and motivated me toward my professional goals.

2. How did you first become interested in technology?

Technology has always fascinated me, and I started delving into it while learning about biomedical engineering in high school.

When conducting research for my PhD, I was completely overwhelmed by the data I got from my research. So, my initial interest in data science and AI was out of necessity.

I wanted to learn how to use all this data faster and more efficiently.

That’s when I started delving into data science a bit more and began learning basic programming skills.

After a couple of years, I realized I loved coding and could take it even further with machine learning (ML) and automate everything in a satisfyingly complex way.

Before the Institute of Data’s Data Science & AI Program, I completed a summer course in Machine Learning in Neuroscience.

It was a global course and an open-source community, so it was a good opportunity to meet like-minded people from different backgrounds.

During that time, I’d established some experience with code analysis and pipelines within my research group. But I still didn’t feel equipped enough to do something from scratch.

3. What motivated you to study Data Science?

After the summer course, I really started appreciating good and efficient code.

That’s when I began practising on my own projects and feeling pretty comfortable with that.

At the same time, I realized my long-term professional goals were changing, and it dawned on me I didn’t want to be stuck in research for much longer.

Because my research was so niche, it was impossible to appreciate the end result properly.

I needed a new direction, not only for my mental health but also for motivation. That’s when I started thinking about entering the health tech space.

I’d spent so much time in the biomedical field and then developed this interest in data science and ML. I thought that’s probably where my skills, background, and interests would combine.

The appeal of the Institute of Data’s Data Science & AI Program was that its course content was driven by industry leaders.

The resources and support available also gave me confidence that I’d be able to complete projects and learn the content.

When I started looking into different programs, I knew that healthcare was the main area I wanted to focus on eventually.

But it was important to gain transferrable skills, and to see how data science can be integrated across different industries.

I spoke to an Institute of Data representative for a career consultation. It’s a big investment of time and money, so I was quite scrutinizing.

I was very impressed with the enthusiasm of the program advisor and how flexible they were in accommodating my needs, which is quite rare and is part of why it worked so well.

The class sizes are quite small, allowing for a flexible, more focused experience. Having smaller cohorts was a big advantage.

My experience with the job support program was positive too, and connecting to industry networks was another massive bonus.

It can be daunting for someone trying to change fields, and when I started, I didn’t have the depth of community in that area.

So having that sense of confidence from the job support program was essential. Knowing they would back me through the whole process was very reassuring.

4. How did you find the program overall?

It was very much driven by the people. I was fortunate enough to be in a class with students who were just as enthusiastic as I was.

[su_quote]Our cohort had real data enthusiasts who wanted to do cutting-edge stuff. [/su_quote]

We were collectively able to drive the program and steer it towards what we wanted to learn, which was a massive benefit, and we were lucky enough to have a great instructor who was very receptive to that.

He was very flexible and had a wealth of experience he could draw on.

There were many tangential discussions, which was one of the greatest aspects of the program.

Our cohort included people with varying degrees and types of experience, so it was great to hear their perspectives.

I wanted to get the best out of the program through instructors in terms of content and knowledge. I used them as a great resource and built good personal connections with the coaches.

The opportunity to follow up and have conversations was invaluable. It was also really helpful because it was flexible, informal, and personalized to me.

Going into a new field can be daunting, especially because of imposter syndrome, which can cause one to not feel like they belong and not even feel like they should apply to certain roles.

I’m so grateful for all of the guidance we received regarding career development, our LinkedIn profiles, etc. It helped me immerse myself in a whole new network and community.

It’s a great community, and I enjoy chatting with those who share my passion for the field. As a result, I’ve also experienced lots of personal growth.

5. That’s great to hear. Can you tell us about your current role?

I’m an education data scientist.

In my role, I have to wear many hats, and it’s not just ML and analysis or pure data science.

The aim is to help educators educate their students better.

That can be done through analytics dashboards and insights or through developing software to help them engage their students better and track student engagement.

It’s a role where I dip my toes into many different areas, which is perfect for someone who likes to learn.

It’s crazy how much I learn daily.

I feel comfortable in the data science lane, but understanding how that fits into the data pipeline and the issues and restrictions the other steps might produce is important.

Being able to delve into those aspects has been super insightful and really helpful going forward.

6. What do you enjoy the most about working in the Data Science industry?

I’m addicted to learning, which is probably why I did PhD.

What excites me about this space is how rapidly it’s evolving, particularly the emerging new tools.

That’s what excites me. I get a lot of motivation from growth and trying to learn new things.

[su_quote]There’s a lot of opportunity for technological innovation in the mental health space. [/su_quote]

Much research is already happening in that space, particularly regarding digital mental health interventions.

I’d like to enter that field, which requires a good understanding of how people behave and how that plays an instrumental role in health conditions.

Another part of that is having a good understanding of how software or digital interventions can be implemented.

I think there’s immense potential in a personalized approach to mental health using innovations and data science.

There are many brain-computer interfaces, like Synchron, that are already making huge advances in treating medical conditions.

I’m excited by the potential of integrating these devices into innovative mental health solutions.

7. What advice would you give to those interested in joining the Data Science industry?

When you’re starting you will have gaps in your experience and skills.

The market is quite tough, and it’s quite competitive, especially at an entry level.

I think it helps not to pigeon-hole yourself into a specific industry or a specific job description.

The field of data science is dynamic, and the roles of data scientists will constantly evolve – so it’s important to be flexible.

Conclusion

If you’d like to learn more about our Data Science & AI programs, book a career consultation with one of our experts at the Institute of Data and start your journey with an actionable plan.

You can connect with Rei and follow his professional journey on LinkedIn.

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