Are you more likely to have a successful academic career as a computational statistician VS a mathematical/theoretical statistician? Advice needed! [C][R]

[u/GayTwink-69]

My professor told me that barely anyone reads or cites mathematical statistics papers compared to computational statistics papers. According to him, it's easier to have a successful academic career if I fully go the comp stat route instead of math stat.

He said his PhD supervisor all the way back in 1990 advised him the same thing. So I imagine it to be truer nowadays with all the advances in AI/ML/technology.

But I honestly love math and math stat and wanna pursue it to the fullest (and in related fields like stochastic processes) but I'm a bit worried that I'll be shooting myself in the foot cause it is objectively harder and I might get cited less compared to if I had done comp stat, therefore leading to a less successful academic career.

[u/Available_Passage_23]

You'd be much better off pursuing a PhD in what you enjoy rather than what's "more popular". I meet so many PhD students who hate their life because they don't enjoy their research.

You won't be limited in your post-doc or career options either route.

[u/Able-Fennel-1228 → u/Available_Passage_23]

I second this. Also id add: a PhD, especially in this field, is not for everyone. Way too many people try for one simply because they “want” one and end up with a substandard degree/insufficient skills or a topic they hate (if they don’t “master out”).

IMO you need four things:

• a burning interest in the topic to bear through the drag that is PhD and life after.
• competence to pursue the topic (doesn’t matter how much you like it, you need to have the requisite computational/mathematical chops to stay afloat consistently in the long run, given the resource/time constraints)
• enough demand for the topic, in terms of the right people/advisor and grant writing opportunities (Of course there are always exceptions, but I’m talking generalities).
• a plan: what is it that you want to do with your degree, exactly what skills will you be offering in industry or academia that will make people want you and how can you build up your CV/portfolio for that? what is your path for long term, consistent progress given the usual uncertainties in everyone’s life?

[u/theArtOfProgramming]

For the love of God choose what you enjoy. Life is too short and a PhD needs to be fun to be worthwhile in my opinion. You’ll work harder on the one you enjoy and will have more creative ideas. We need passion in the math side just as much as we need it on the computational side.

[u/eeaxoe]

Define successful. That word means different things to different people.

[u/GayTwink-69 → u/eeaxoe]

Well for one, tenure

[u/jarboxing]

I think mathematical statistics is gonna play an important role in the comp stats field. I had a situation recently with a comp stats guy that was trying to reduce the memory he needed to fit a model. He was passing all the data through the pipeline. I suggested he use the exponential family in order to obtain a sufficient statistic, and then just pass that statistic through the pipeline. He looked at me like I had an antennae.

[u/Training_Advantage21]

AI/ML/Data Science is over saturated. Everybody and their dog are trying to get into that space. There are billions of people (nearly) with relevant degrees/masters, people from adjacent fields etc. This is in industry but it also affects academia as the supply pipeline is the same, some people give up after PhD and go to industry, some stay in academia.

If your personal interest is on the theoretical side, pursue your theoretical interest. A PhD is a long time and an academic career even longer, do things that you find interesting.

[u/Ordinary_Machine215]

I would say both are fine if you really like and enjoy the field and want to go to academia. Many people know computational/applied statistics research is more likely to be published and cited more frequently. Equivalently, sometimes theoretical people can get an academic job even with less papers or citations.

However, many departments want to hire people who can get grants, and probably theoretical statistics has less chance (I might be wrong). Moreover, if you want to go to industry, then computational statistics might be better to find a job.

[u/latent_threader]

I think it really depends on how you define “successful.” Computational statistics tends to get more citations because it’s easier for others to directly apply your work, especially with AI/ML trends. But if you love math stat, pursuing it deeply can still lead to a meaningful academic career, especially in theoretical areas that intersect with stochastic processes or probability. Sometimes success comes from carving out a niche where your work is genuinely valued, rather than chasing citation counts. Balancing your interests with applied visibility could be a good middle ground.

[u/GayTwink-69 → u/latent_threader]

If the more theoretical papers are cited less, does that mean you have to work much harder as a theory person than a computational person to reach the same level of success (measured in either position, salary, benefits, or work life balance)

[u/adlersmut089]

You might have better luck among actuaries. I got burnt out in Finance though.