Math + CS vs Math + Econ at UdeM – grad school and career implications, which route to choose today?

Hi!

Recently got accepted into both Math + CS and Math + Econ at the University of Montreal, and I wanted opinions from people with more industry / academia exposure than I currently have.

As context:

– I’m planning on pursuing grad school regardless of path, at minimum a master’s and potentially a PhD.

– On the Math + CS side, I’d mainly target Data Science / AI / ML and potentially research oriented work.

– On the Math + Econ side, I’d mainly target computational finance / quantitative economics, while remaining open to banking, policy, higher-level finance/accounting and other people facing roles.

– My background includes a fair amount of self taught programming, both lower level / performance oriented work (embedded, systems) and some data engineering / ML fundamentals, which I genuinely enjoyed.

Also, assume strong academic performance as baseline for the discussion (top ranking student / around 4.3 GPA ), mostly because it matters for grad school related advice. I'm also contantly planning and woking on self learning and self guided projects; whatever I do I actively try to make my hobby.

A lot of my hesitation comes from these points:

– I feel it may be easier to stand out in Data Science through academic research, projects, publications / self driven demonstrable technical work.

– UdeM seems like a uniquely strong opportunity for AI research given Mila and the broader ecosystem, while also being solid for computational finance.

– I’m more flexible career wise on the Econ side; I’m not interested in traditional software engineering anymore, whereas economics/finance seems to leave more safe doors open.

– I’m apprehensive about both the volatility/hype based demand around AI markets and the heavy credential/networking culture in finance.

– From the outside, it feels like raw technical skill and research output carry more weight in DS/AI than they do in finance, where signaling/networking/internship name prestige seem more dominant. (Could be wrong here, please correct me if I am)

– I also feel a master’s adjacent to economics (computational econ/econometrics/quant finance) may go proportionally farther and yield more interesting opportunities than a DS master’s, where PhDs seem much more common at the higher end research level I like.

– One of my fears is that on the DS/AI route I’m effectively betting on landing something like a Mila PhD/research trajectory to fully justify the path long term.

– I’m also concerned about AI’s impact over the next 5-10 years and whether higher level economic/policy/finance roles might ultimately be more resilient than technical DS work.

Long story short:

– I love applied mathematics and independent research.

– I’m genuinely interested in both economics/markets and data science.

– I can see myself enjoying either path ( I like everything, it's somewhat a major flaw of mine).

What I struggle distinguishing, given I see fun in both avenues:

– the borderline “dream like” but potentially high upside AI/data path

– the seemingly more rigorous and institutionally stable economics/finance route.

Would especially appreciate perspectives from:

– people in quant / computational finance or finance more broadly

– AI or DS workers and researchers

– econ grad students

– poeple with insight on either job market

– people who faced / are facing a similar decision

Apologies for the post length, I hope it's digestible enough. I'm quite frankly lost in my decision making, I think I structured my thoughts well enough but can't seem to go past bringing out the main contention points, hece why I'm seeking for some insider advising and insight.

Thanks a lot! I immensly appreciate any insight and your time.