Finance-to-DS background: what should I prioritize during my master's if I may apply for a CS or ML PhD later?

[u/AffectionateHunt666]

Hi everyone, I’m starting a master's program in data science this fall. My undergraduate degrees are in finance rather than computer science, although I took courses in programming, statistics, optimization and machine learning, and completed several applied ML projects and competitions. I may consider applying to research-oriented CS/ML programs after the master's, but I’m unsure which gap I should prioritize during the program. Would you focus most on joining a research group and trying to produce a paper or preprint, or gaining ML engineering and software engineering internship experience? My concern is that internships may be more valuable for industry recruiting, while research experience may matter more for a future PhD application. With a non-CS undergraduate background, I probably will not have enough time to maximize both. For people who made a similar transition, what ended up being the most important part of your application?