Agustin Suarez '24MSDS

What inspired you to pursue a Master’s in Data Science at Bryant?

The field itself. Data science is a hot topic. It’s quite interesting to learn about machine learning, and, you know, AI is booming right now. So those topics really caught my interest. Also, I had the opportunity to join the tennis team. So the subject itself, the tennis team, and Bryant itself is a great place to pursue a master’s degree -- all three things combined.

What specific skills or experiences from the MSDS program have you found most valuable in your role as a software engineer at Google?

Specific to Bryant’s master’s program is the skill of being organized, and being a self-starter-- this is a big deal here. At Google, you’re surrounded by probably the most brilliant people in the world and you have to find your way in. You’re guaranteed that there’s going to be someone within reach that is going to know better on any given topic. So you have to be on the lookout for new things to make sure that you’re learning what is actually being used because things here are very dynamic and they change. For example, if you try to read a presentation on a specific topic that was done a year ago (and I’ve already run into this) it may be outdated. So being organized, being a self-starter, and having good time management are skills specific to Bryant that have served me well at Google.

What was the biggest challenge you faced during your studies, and how did the program support you in overcoming it?

As a master’s program, you have people from all over. You have people with a computer science degree (like myself), other people coming from actuary, others coming with a strong math background, some coming from engineering. So figuring out how to work as a group while having different backgrounds was a challenge. But the groupwork incentivized at Bryant was great and helped me a lot. My classmates were open and willing to help me out with things that I struggled with. Especially, in terms of time management. As an athlete at Bryant, I played in tennis teams, so I was away quite a bit, and my group mates were flexible with my schedule. So that was really valuable.

Are there any specific projects or hands-on experiences during the program that you feel helped you stand out in your job search?

Two projects really helped me a lot in terms of my learning progression. We did a machine learning project to analyze
which job search skills were the most valuable. That was during my first semester at Bryant. It was a great project, really helped me out in terms of understanding machine learning by itself.

The last project was great as well. I developed a machine learning model that basically helped AAA to better understand what they were missing on when we analyzed their data. It was really nice for an actual company to give us a real data set to work on.

The tech industry is competitive—what advice would you give to prospective or current Bryant MSDS students or recent graduates who are hoping to break into top companies like Google?

I firmly believe that if you really want it and you work for it, maybe you’re not going to get the best, but you’re going to get something close to it. For me, that was the goal. In terms of advice, you have to be on top of things. You have to be willing to spend a lot of time studying.

The interviews for tech companies are very tough, very competitive. Just the fact that you’re able to get an interview is really good. Being organized, being able to prioritize things is important. I know that oftentimes people in college are looking to have a great time, but the week is seven days. You can have fun one or two days during the week and other days you have to make sure that you’re doing things that will help you out in your ultimate
goals.

Also, reaching out to people. I didn’t actually have any kind of connection to Google, I’m completely new here, but I reached out to alumni from Bryant. And even though that didn’t lead to a direct referral to Google, it helped me to get to know the field.

You can also ask for a mock interview, because failing interviews is really common. I actually failed a big interview with Amazon because I didn’t do enough practice, even though it was easier than my interview with Google. You’re much better off failing the mock interview rather than failing the actual interview. It’s just for you to review and see what you’re missing. I did a mock interview with an ex-Meta engineer, and that helped me out a lot in terms of knowing what I should I work on next because there’s so many topics that you have to study.

How has your perspective on data science and software engineering evolved from your time at Bryant to now working in the field?

My perspective has changed. I’m going to give an example of AI, let’s say Gemini. Gemini is a product of Google. From outside, you will think there’s a team that just does the Gemini core model and that’s it. That’s how I thought about it.

But for Gemini to work, there’s a lot of teams working together to make that ultimate product. What shocked me the most once I came on was the size and the spectrum of this product. I’m on a team of 30 people and all of us are committed to one small product in the whole Google environment. And these 30 people are part of another group that has another three teams that have 30 people each. And that team is part of another bigger thing. The size of the actual work is just incredible.

Looking back, is there anything about the program or your approach to it that you think played a key role in landing this position?

The most valuable thing for me during college was to be able to understand how I learned best. You can learn a lot about software engineering during your college years, as well as what machine learning models tell, such as what the goals are, how to create a model, everything like that.

But at companies like Google, there are so many internal tools, and there’s just no way of learning them from the outside. Right now I’m learning all these tools specific to Google and it’s like learning computer science and data science all over again.

I talked with a few students the other day and they were most interested in machine learning. And I think that it’s really important to learn those core models, but it’s also really important to know they’re just core models and the actual, most successful models are highly, highly fine-tuned. They’ve been researched, they’ve been developed from zero up.

So what you learn in school is a great stepping stone to be able to learn all these other things because again, everything is internal. And knowing how you learn is a great, great skill to have.

What are you most excited about in your new role, and how do you see your skills growing at Google?

The most exciting thing for me is knowing that everything that I work on here touches the lives of millions, if not billions of people because Google is such a big company. It’s part of everyone’s everyday life, whether they want it or not. Knowing that the work I’m doing is going to help improve the lives of a lot of people is really exciting. Also, I’m at the forefront of technological development here. I’m able to stay up-to-date just by doing my job. It’s awesome!

I’m becoming a much better engineer day by day. But also, I’m learning how to be a better person. Soft skills here at the company are really important. The people here are so kind and open to teaching you stuff and showing you how it works. Learning how to be like that is a really valuable skill, whether the future takes me within Google or somewhere else.

Finally, what advice would you give to those considering a graduate degree in data science at Bryant?

I think it’s a great program. The teachers at Bryant really care about your learning, especially Professor Dimas, she was incredible. She would take time out of her own day to continue teaching us and that’s just something that you don’t see ever in any other place. That’s one specific case, but overall, the professors really care. They are flexible for you to learn on your own terms, and I think that’s great. Incoming students should know that if you want to learn a lot, you can. It’s something that helped me a lot, and I definitely recommend it.