“Courses emphasized not just how to build models, but how to think critically about data, assumptions, and real-world constraints. That mindset has been invaluable in an industry setting where clarity, communication, and impact matter just as much as technical accuracy.“
About You & Your Work
What is your current role and organization?
Catastrophe Modeling Analyst at Aon.
Can you share a bit about your career journey since graduating from the Master of Analytics program?
After completing the Master of Analytics program, I transitioned into a full-time role at Aon following a summer internship with the same team. Since joining full-time, I’ve been working closely with engineers, scientists, and business stakeholders to build scalable data pipelines, analytical tools, and dashboards that support reinsurance brokerage to the market and database management.
What are you working on now that you find most interesting or impactful?
I’m particularly excited about projects that combine data science with geospatial data, including improving insurance industry exposure databases quality and building tools that help translate complex risk data into insights. I enjoy work where analytics directly informs strategy and decision-making.
What do you enjoy doing outside of work?
Outside of work, I enjoy hiking and exploring night sky scenes.
Connection to the Program
How did the Master of Analytics program help prepare you for your current role?
The program provided a strong balance between rigorous technical training and applied problem-solving. Courses emphasized not just how to build models, but how to think critically about data, assumptions, and real-world constraints. That mindset has been invaluable in an industry setting where clarity, communication, and impact matter just as much as technical accuracy.
Was there a class, project, or experience in the program that particularly shaped your career path?
Project-based courses stood out the most for me. Working on end-to-end analytics projects — from defining the problem to presenting results — closely mirrored the kind of work I now do professionally. Those experiences made the transition to industry much smoother.
Specifically, the database management course helped me build a strong data engineering foundation, which later translated directly into cloud database management and hands-on experience with platforms I now use daily, such as Databricks and Snowflake. In addition, the machine learning and deep learning courses were invaluable in giving me applied experience tackling real-world analytical problems — from model development to evaluation and communication — which has been especially relevant in my current role.
Reflections & Advice
What advice would you give to prospective or incoming Master of Analytics students?
The job market can be challenging, and that experience encouraged me to broaden my perspective and explore how analytics is applied across different industries. Like many students, I initially aspired to pursue a data science role at a major tech firm. Over time, I realized that impactful data science work extends far beyond the tech industry, often in areas that require some exploration to uncover. Reinsurance and catastrophe modeling, for example, were fields I had never encountered before entering the program, but through intentional industry exploration, I found them to be an excellent fit for my interests in risk management, data science, and engineering.
What skills or mindset do you think are most important for analytics professionals today?
Beyond technical skills, adaptability and clear communication are critical. Tools and technologies evolve quickly, but the ability to learn continuously, ask the right questions, and translate data into meaningful insights will always be valuable. These are also the skills that tend to carry over most strongly into interviews.
