headshot of Tigran Melkonian

Q&A with Tigran Melkonian, MS in Applied Mathematics

What is your major, and when are you graduating?  

I’m pursuing a Master of Science in Applied Mathematics, with a specialization in data science. I will be graduating in spring 2022! 

Why did you decide to enroll at Northeastern University and pursue an advanced degree in mathematics?  

Northeastern University’s experiential learning program was the primary reason I decided to enroll as a graduate student. The experiential orientation of the program has not only allowed me to acquire a clear understanding of abstract graduate-level mathematics but, most importantly, has provided me with the opportunity to concurrently apply my learnings and continue fulfilling my potential as an aspiring data scientist through real-world industry experience. 

What is your experience in the program like so far?  

My experience as a graduate mathematics student has exceeded all of my expectations. The faculty I have had a chance to interact with all seem like they genuinely care about your personal and professional success. As a result, pursuing an advanced degree here has been one of the best decisions I’ve made.  

Did a COS faculty or staff member help you excel in this program?  

All of my professors have had a hand in helping me excel at Northeastern! I would also like to give a special thanks to Patty Corrigan for her help during my co-op search last spring. 

What is your favorite course in your program? Why?   

The most interesting course in my program was MA 7243 machine learning and statistical theory with Professor Nathaniel Bade because it provided a practical end-to-end overview of the machine learning development pipeline, from problem inception and data acquisition to model training and validation. I could not recommend this course enough to students interested in learning machine learning hands-on. 

Tell us about your co-op experiences.  

I’m currently working as a Data Analytics Co-op, on the Global Safety and Support Tools team, at Amazon Robotics! My team’s goal is to proactively identify opportunities for improvement within the Amazon Robotics solution by developing software tools that support and further automate operations across our global Fulfillment and Transportation Centers network. I work closely with software engineers, product managers, system engineers, and data engineers to develop, validate, and deploy operational data models and metrics. Since the start of my co-op, I’ve had the opportunity to take full ownership of the development and deployment of an operational performance anomaly detection model to support the solutions that facilitate Robin (robot arm) issue ticketing and resolution for automated sortation centers. I have definitely learned a lot of new practical skills that I would not have been able to learn in a classroom, but I also find myself applying what I have learned as a graduate mathematics student on a daily basis. This co-op has served as the perfect complement to my graduate studies by helping deepen my understanding of complex subjects such as mathematical modeling, numerical analysis, machine learning, and probability through first-hand experience.   

Do you have any advice for currently enrolled students pursuing co-ops?  

The process of pursuing co-ops can be stressful, but it’s important to remember that you’re not alone, so make sure you reach out to your co-op advisor and professors as early as possible so that they can help guide your search and provide the feedback you need to be the best version of yourself. Also, quality over quantity of applications is the right approach for guaranteeing a successful co-op search. Finally, research the companies you are interested in working for and carefully tailor your resume and cover letter to reflect a company’s respective needs.  

How have your classes enhanced your co-op experiences?  

They enhance each other! My classes help me build a solid foundational understanding of the mathematics behind virtually all data science methodologies necessary for modeling complex system processes and prescribing sound data solutions. My co-op experience helps me frame the application mindset required to truly understand the theoretical concepts discussed in class. 

What are your post-graduation plans?  

After graduating, I’m planning to pursue a full-time data science role. 

Do you have any advice for graduate students looking for work experience in similar fields?   

Yes! It’s important to note that you do not necessarily need to major in applied mathematics or computer science to become a data scientist. That being said, you should have a solid understanding of mathematics and use R or Python for data manipulation and analysis. For graduate students interested in data science who don’t already have direct industry experience, it’s crucial to build up your professional portfolio. The best way to achieve this is to complete a set of personal data science-related projects in problem spaces that interest you. Visit sites like Kaggle to explore data-sets that interest you and take inspiration from the projects others have already completed in the problem-space of your interest. The most natural and powerful form of learning is through hands-on experience, learning by doing, so just dive in!