When Sohayib Fahim moved from Bangladesh to St. John’s to study computer science at Memorial, he didn’t know exactly where it would lead — only that he was excited to learn.

Four years later, the new spring graduate has built a strong foundation in data science, machine learning and software development.
A key part of that growth came through his work with Memorial’s Regional Analytics Lab (RAnLab), where he served as the lead developer on a new online data portal.
The portal is designed to help municipal leaders better access local data.
Mr. Fahim worked closely with the RAnLab team and other students to build the site from the ground up, turning a prototype into a functioning platform that’s already supporting real-world decision making.
“He brought a level of focus that pushed the project forward,” said Jamie Ward, manager of the RAnLab, of the new Memorial alumnus. “It was great to see him grow throughout development. He took on more responsibility as things progressed and really became a key part of the team.”
Complex problem-solving
Reflecting on his time at Memorial, Mr. Fahim points to the importance of collaborative learning and real-world application.
“I really enjoyed collaborating with talented peers and mentors at Memorial, especially during my time at RAnLab, where I deepened my skills in data science, database management and hands-on problem solving,” he said.
In addition to completing his bachelor of science degree, he also completed an honours thesis in natural language processing and machine learning.
“I learned the value of persistence, interdisciplinary thinking and turning research into real-world impact.”
He says both experiences — research and applied development — taught him how to think through challenges and stick with complex problems.
“I learned the value of persistence, interdisciplinary thinking and turning research into real-world impact.”
So what’s his plan for the near future?
Mr. Fahim hopes to work as a data analyst and is also considering graduate studies in data science or predictive modelling.