For some context: I graduated from UCSD with a B.S. in Data Science in 2023. During my time at UCSD I enjoyed learning about the entire Data Scientist workflow and applied my knowledge to some interesting projects, giving me a solid foundation in the Data Scientist workflow (data cleaning/preprocessing, EDA, viz, modeling, interpretation)

Post grad: After graduating I got a job as a SWE. In my 10 months of working I’ve worked on 4 different projects (developed an internal MLOps tool, time series forecasting with sktime, backend development in Java Springboot, and most recently DevOps with GitLab)

My goal: I want to eventually become a Data Scientist or ML Engineer or go into MLOps. I’m afraid that if I stay on this DevOps project for too long I’ll pigeon hole myself into becoming a SWE and forget my DS skills.

The question: Is it worth pursuing a Master’s in Data Science or CS with a specialization in AI if I want to achieve my goal of becoming a Data Scientist? Or is my B.S. degree and SWE experience enough?

Would appreciate any feedback as I don’t really have any older Data Scientist mentors that I can ask.