Admir – the golden boy.

What’s better than a gold medallist? A double-gold medallist! And Finshark just hired THE guy, straight out of Sarajevo University. Finishing his studies with an average of 10.0 (that’s on a scale of 10) he’s joining our ranks as a Data scientist. Read along as we talk with Admir about his new job and what it’s like leaping from academia to business.

Hello Admir, welcome to Finshark! Your reputation may precede you, but let’s start by telling us about yourself.

Throughout high school, I participated in math and physics competitions, laying the foundation for my academic journey. I have a master’s degree in theoretical physics and have been awarded as the top student at the University of Sarajevo with the prestigious gold badge twice. Having worked in academia, I now find a more profound calling in advancing my professional journey through my passion for data science.

Uhm, wow! I have so many questions. But first; What’s it like taking the step from the academic to the business world? Did you have any expectations or fears that were deemed true or false? 

Transitioning from academia to the business world was an exciting shift for me. I expected a faster-paced environment with a stronger focus on practical applications and that’s exactly what I got. Even though it was a little scary in the beginning, the experience has been fulfilling, and any initial concerns were outweighed by the opportunity to expand my knowledge here.

Also, I have to ask! Not only did you receive the gold badge from the University of Sarajevo, but you got it TWICE?! It sure sounds like a big deal, but what is it? Give us all the juicy details!

The gold badge is given to students with a GPA exceeding 9.5 in the Bosnian grading system. I was awarded as the student with the highest GPA in both my bachelor’s and master’s studies at the University of Sarajevo, earning the gold badge on two separate occasions.

How did you celebrate your double gold?

It involved a couple of large dinners with my family and friends.

And now we can welcome you to Finshark. Amazing! Tell us about your role here.

I am a Data scientist at Finshark which means that I collect, transform and analyze large datasets to extract insights, build predictive models and uncover meaningful information.

That sounds advanced! What type of meaningful information are we talking about? Can you give us an example?

There is a lot we can do with data science. At Finshark, I often work on uncovering valuable insights such as spending behaviour patterns and predictive models to assist in making informed decisions and also provide that information to our customers.

What are you currently working on? Anything exciting?

Very exciting! I’m currently working on machine learning classification models which categorise transactions.

There’s a lot of talk about AI right now, not just in fintech, but… well, everywhere. Is AI and machine learning, the same thing? If not, what’s the difference between AI and machine learning?

You can think of AI as a broader concept that encompasses machine learning, but also a lot of different technologies used for creating machines that can do tasks that typically require humans. Machine learning is a subset of AI which uses statistical methods to enable machines to learn from data and do tasks without being explicitly programmed.

Is there anything in particular that do you like the most about your new job?

Seeing a machine learning model you’ve worked on for a long time finally perform well and being ready for production is amazing!

Well, I’m sure we’re all eager to see that! If you look into the near future, what’s your ultimate work ambition?

My ultimate work ambition is to continually learn, broaden my knowledge, and reach a level of proficiency where I can independently execute entire machine learning projects.

You know what? I’m pretty confident you will get there in no time! Before we round this off and let you go back to learning machines to do math, do you have any words of wisdom you would like to share with us?

Just don’t settle for mediocrity. Strive to be the best at something, but also make sure to have time for friends and family.