Stat Assistant Professor

University of Iowa's Statistics PhD program has a concentration in actuarial science/financial mathematics. UCSB and Rice University also have PhD programs in Statistics with a financial math concentration. These are good programs to consider. Additionally, you can also do an Actuarial Science concentration in a few Mathematics departments, e.g. at UIUC and UConn: https://math.illinois.edu/academics/actuarial-science/graduate-degrees/phd-mathematics-concentration-actuarial-science-and https://www.math.uconn.edu/degree-programs/graduate/ph-d-in-actuarial-science/

This shouldn't be a problem. I started applying for PhD programs about 1.5 years after I graduated from my MS program and had not kept in touch with any of the professors I asked for rec letters. Just shoot them an email with a short refresher of yourself and ask them if they would be able to write you a strong recommendation letter. And if they agree, then send them a more detailed email with all of the work you did for the professor.

1) Indeed, the most important thing for getting a job in academic statistics is to publish in good journals and conferences. In statistics departments, articles appearing in top journals (JASA, Biometrika, Biometrics, JRSS-B, AoS) and top-tier conferences (NIPS, AISTATS, etc.) carry a huge amount of weight both towards getting hired and your tenure case. Pedigree is helpful, but the publication record matters more. 2) Hiring decisions in math, CS, and statistics will often be made based on filling a particular "niche" that the department is looking to expand or that it currently lacks. My PhD department did not have any faculty specializing in Spatial/Environmental Statistics, so they hired a new Assistant Prof whose research was on that this past year (they even put out a job ad saying they were looking to hire a new Assistant Professor in the area of spatial statistics). Right now, they are looking to expand the number of faculty in Data Science and Statistical Network models. As to whether to hire a theoretician or a more applied statistician, it may also come down to whether the hiring Department thinks you will fit into their "culture." Some departments are very theoretical and plan to stay that way. My Department has traditionally been rather theoretical, but right now, it seems to want to branch out a bit. In any case, there is a job search committee, and the hiring decisions are made collectively by the committee (so some faculty may prefer the theoreticians, but if enough faculty prefer the more applied statistician, then they will extend the offer to the applied statistician). Typically, there is a "shortlist" of 5 candidates, with the top 3 invited to campus to give job talks as departmental seminars. Then the top 3 are ranked, and if the top choice declines, then they extend the offer to the second choice, the the third if necessary. If all three decline, then they may choose to invite choices #4 to campus, and #5 if necessary. If *no one* accepts the job offer, then it is declared as a failed search. 3) Securing external grants certainly helps and is counted towards your tenure. Additionally, in Math and Statistics, the salaries are for 9-month contracts. If you want to get paid in the summer, you need a grant. Plus, if you get grant money to pay part of your salary, you can "buy out" of teaching for one semester or two, or use it to fund more of your students and your conference trips. So most Statistics professors will apply for grants. But I would say the pressure to bring in grant money is less than in other STEM areas.

1 & 2) Your profile looks very strong, and I believe that you will get into a top PhD program. However, it may be a good idea to add a few larger programs like NCSU, Penn State, or Purdue. However, I anticipate that you will get accepted to at least one school on your list at the level of CMU or Duke, possibly higher. 3) The academic job market is much better in Statistics than in Mathematics, but still competitive. It is possible to get a tenure-track job without doing a postdoc, but usually, this requires a getting a publication or two in one of the top Statistics journals (Annals of Statistics, JRSS-B, JASA, or Biometrika) as a PhD student. Even if a PhD student manages this feat, a lot of PhD graduates interested in academia still choose to do postdocs, since a productive postdoc can give them more employment options if it goes well and gives them a chance to focus solely on research -- thereby easing the transition from PhD student to Assistant Professor, where you have to spend a lot of time preparing for teaching. My PhD advisor actually graduated a student the year before me who had gotten a TT job offer at University of Arkansas, but this graduate turned down the job in favor of a 2-year postdoc in Statistics at Columbia University. Doing a postdoc seems to be the norm, however, even for graduates of elite programs like Berkeley, UPenn, and Harvard. The research area does play a role, but not so much w.r.t. theory vs. applied (unless the department looking to hire a new Prof is tilted in one direction or the other). Hiring decisions are much more based on whether or not the research area is relevant and of current interest. My PhD granting institution has been on a hiring spree in recent years (looking to hire three more Assistant Professors this year), and the job ads explicitly states a preference for candidates with expertise in machine learning, network analysis, and data science. Within these areas, one can work on either solely applications, hardcore theory, or a mix of the two. Of the four professors my PhD school hired this past year, two were in applied areas and two were in theory.

I think you can probably get into most of those Statistics Masters programs. UCLA is one of the top Applied Math programs in the country, and your Math GPA and overall GPA both seem to be well above the minimum threshold for Masters programs.

I think the top PhD programs in Applied Math (NYU Courant Institute, UCLA, etc.) will be quite competitive, and most of the admitted students will have already taken a number of PhD level mathematics courses. This is not necessarily the case for Statistics. I think once you get below the top tier (e.g. NYU in Applied Math and Stanford in Statistics), Statistics and Applied Mathematics PhD programs are about equally competitive. Admissions are holistic, and your profile is pretty good. Maybe try to improve your overall GPA as much as possible by the fall of next year and get great recommendation letters, and you could be admitted to a really good school. But given that you're from Princeton with excellent math grades, I would guess that your application will be "in the discussion" at most schools you choose to apply to.

You may want to ask your letter of recommendation letter writers to explicitly point out your real analysis and linear algebra backgrounds in their letters (which seems more than sufficient for doctoral study in Stats), so that there is no doubt in your application. Your LOR writers should also point out that the statistics class you took was based on Casella and Berger and that you took advanced probability theory. You can also mention this in your statement of purpose or provide a description of your coursework in a supplemental document, but the letters of recommendation will be read more closely so this should definitely be mentioned in there. It is true that there is a lot of competition for international applicants, but I think that mainly applies to Chinese and Indian applicants because of the sheer volume of qualified applicants from these countries. I do not think there are many applicants from Italy, and plus, you go to a famous university. I think adcoms would also expect an Economics degree from a top school in Europe to be pretty technically rigorous. Statistics schools in the range of 10-30 (for statistics only) and 20-60 (in aggregate USNWR rankings) are a good range to target, but you can also apply to one or two "reach" programs like Duke or University of Washington.

You don't need to retake the GRE. For the UK and European schools, their PhD programs do not contain any coursework. And based on my familiarity with them and the statistics papers I've read from academics there, their research focus tends to be extremely theoretical (these schools' Business and Economics departments publish the more "applied" statistics papers in applied statistics journals, but their statistics depts seem to be very heavily focused on theory). So for the top schools there like UCL, Imperial, etc., i imagine that they might prefer to enroll students who have more extensive mathematics backgrounds -- especially since their doctoral programs don't have any coursework to bring you up to speed, and your background may fall short. To have a shot at these, it may be advisable to do an MS in statistics or mathematics. I think you have a shot at some U.S. Statistics programs. Yale and UPenn might be difficult since these programs admit very few students a year and have a preference for those with very strong math backgrounds. But the others on your list seem plausible with your background (Duke might be a bit of a reach but I wouldn't say it is impossible for you to break either).

Don't report it.

Do you know if your "smallish" Canadian school has a well-regarded reputation in the U.S.? Your profile looks pretty strong, even without the math subject GRE, but your list of schools is a bit top-heavy. I would recommend adding a few schools like Purdue, Iowa State, or NCSU, if you have the funds. Also agreed with the above, a 70th percentile should be okay. In addition, many schools don't care about the math subject GRE, and I would only advise taking it if there is a major deficiency in the application (like relatively low math GPA) or if it's required as part of the application (only a select few schools). Duke even explicitly states on their PhD Program FAQs page that they do not consider the subject GRE scores.

True, but if we are talking about students entering PhD programs in Stat in the U.S. who only have Bachelor's degrees, the average international student will have had more preparation than the average domestic student. They do have somewhat of an advantage in the coursework phase. But none of this matters that much as long as the PhD qualifying exams are passed, and breadth of mathematical preparation does not confer any special advantages when it comes to research (as opposed to classes/exams).

The cumulative GPA is a bit lower, but your math GPA and your GRE subject test score are superb. And you went to Princeton which has the best mathematics department in the country. I think you are in good shape to get admitted to a top Statistics program (if that's what you're interested in?)

Yes, it does seem like the undergraduate curriculum in other countries is more advanced than in the USA... I think most of the international students in my PhD cohort had already taken most of the required courses for my graduate program prior to enrolling (including the Casella-Berger sequence, linear models, measure theory, etc.) and were merely repeating the classes . No big deal, domestic students and international students are on equal footing once the research phase of the program starts. But international students do tend to have a bit of a "headstart" in the coursework phase, unless the domestic student already has a Masters in Statistics.

Yes, you should include anything that is taken above the level of Calculus I-III and a first course in Linear Algebra (which they the adcoms will be able to see on your transcript). I assume that you are an international student. Indeed, a course like Real Analysis is a lower division class in many foreign institutions (e.g. in China, many of the math students take analysis starting their freshman year and then have to take multiple semesters of it). But in the United States, real analysis is considered an upper division course. Real analysis, differential equations, discrete math/a first course in proofs, and advanced Linear Algebra (with proofs) could all be considered upper division math classes in the U.S. Any probability and statistics classes that involve Calculus should also be listed in your application as an "upper division" course. You could even list Regression and Design as upper division courses if you've taken them, even though at the undergraduate level, these types of classes do not tend to involve much higher level math (since these classes require some familiarity with matrix algebra/calculus and distributional theory to really study at a rigorous theoretical level).

I'm not sure that "high-dimensional statistics" is very specific, but in general, it is a good idea to keep the statement of purpose fairly generic, UNLESS you're one of the rare candidates who has research experience that resulted in publications in statistics journals. Either way, though, I don't think the statement of purpose is the dealbreaker. There isn't much you can do about your GPA at this point, and the GRE is only a "sanity check." So assuming that those are decent (and I assume they were well above average, since you were waitlisted at top 10 programs), I would focus on what you *can* control (i.e., the letters of recommendation). Make sure those are topnotch (i.e. that your letter writers will refer to you as one of the best students they've ever taught,), make sure to provide your letter writers with a very detailed description of what you did/accomplished in their classes so they can tailor the letters for you, etc. It's fine to reapply to schools that previously waitlisted/rejected you. You may be lucky and be able to crack them the second time. But just remember to apply to a wider range of programs this time, not just the top 10 PhD programs. The top tier is difficult for even people with 3.7+ GPAs and near-perfect mathematics subject GRE scores to get into -- and based on what I've seen one such top 10 Statistics program, many of these programs seem to heavily weigh the prestige of the undergraduate institution (the domestic students I met were from MIT, Stanford, Yale, etc.).

Publish in a respectable journal (or journals) and you will be in good shape to secure a top postdoc. Publish a first author paper in Biometrika, JASA, JRSS-B, or Annals of Statistics, and you might even be able to get a faculty job right after finishing the PhD. (For biostatistics, a publication in Biometrics would probably put you in very good shape too). That's what the most important thing for the academic job market is.

It seems like University of Georgia and University of Kentucky have funded MS programs for statistics: http://jlmartin.faculty.ku.edu/~jlmartin/masters.html Also, it seems like there are many more funded Masters programs in Mathematics than there are in Statistics, since there is always a need for graduate math TA's for the big Intro Pre-Calc and Calculus classes. I obtained an MS in Applied Math and it was completely funded with stipend in exchange for TA'ship. Two of the other Statistics PhD students from my PhD department also completed funded Masters degrees in math (one at Ohio State and one at Wake Forest), and one of my PhD advisor's former students did a completely funded Math MS degree at Clemson (and this person is now an Assistant Prof at a top 10 Stats program). If money is a concern, it may be worthwhile to consider MS programs in Mathematics or Applied Mathematics, where you load up on statistics classes as electives (that's what I did -- I took 4 Masters levels classes in Statistics, including the Casella & Berger sequence and a few applied classes). The student who got his Masters in Mathematics from Ohio State also took the Casella & Berger sequence, in addition to the required math courses for the MS degree, so he was well-prepared for the Statistics PhD program.

I think as it stands, you can probably get admitted to a school at the level of Iowa State or Texas A&M -- possibly higher, given your pedigree and your strong performance in your Masters program. I don't think you would be at a disadvantage for a program because you had applied to it and gotten rejected previously (if anything, the adcom would take into consideration your most recent academic performance this time around, and your performance in your Masters program is very good). As others have mentioned, however, Duke, CMU, Harvard, Berkeley, etc. are just very difficult to get into to begin with (even a lot of applicants with 3.8+ GPA's get rejected from those schools too...), so there's no guarantee you can be admitted to one of those programs. It would be a good idea to apply to a wider range of schools, including schools like FSU, UT-Austin, and OSU.

Those are all very good schools overall, but I am not sure how their Statistics departments compare to each other. TBH, when it comes to the UK and Europe, I am more familiar with individual "big shot" names (e.g. van de Geer, van der Vaart, etc.) than I am with the departments themselves. It's often been said here that the PhD advisor's reputation matters more than the institution. I would have to agree. Someone with a renowned professor as their PhD advisor and one or two papers in respectable journals has a very good chance at securing a top postdoc in the U.S. Even if the advisor is not as well-known, having one paper published, accepted, or in revision at JRSS-B, JASA, AoS, or Biometrika gives you a huge advantage in the academic job market. My department extended a TT job offer to someone this past year mainly because they were the single author for an Annals paper (this person ultimately accepted a TT job offer from Purdue) -- this person was from Michigan State, if I recall correctly. TL;DR: the quality of publications and the recommendation letters from PhD advisor(s) and postdoc mentors seem to matter the most for academic employment. PhD granting institution is secondary to these two things.

I don't have any personal experience with schools below the tier that I mentioned, but assuming that you have acceptable GRE scores (which really only serves as a "sanity check") and decent to strong recommendation letters, I would consider Ohio State a reach school for you. I looked at the Results page for some of the schools, and it seems like you *might* have a shot at some places (e.g. for UT Austin, I see that someone was admitted to their Statistics PhD program with a 3.2 GPA). I wouldn't say that this is very common though, and we can only speculate (e.g. it's possible that this applicant did very poorly their first few years but then aced all their classes in their junior and senior year with high grades in all their math classes). Most of the acceptances are from people who report having higher GPAs. As I said, if you have the funds, you can apply to a few PhD programs, but I think that it would be best to prioritize Masters programs.

Your major GPA is a bit lower than the most competitive applicants. If your GPA in your math/stat classes were ~3.8 or higher, then your chances would have been better, even with the 3.5 overall GPA. With a 3.5 GPA in math classes, that suggests to me that you had about an even spread of A's and B's in math classes, and most competitive candidates would have more A's than B's. I think your chances would dramatically improve if you completed a Masters degree first and excelled there. With a Masters degree and strong performance there, you could probably get into one of the PhD programs in the 20-40 range for Stats (not the pooled USNWR ranking but the separated list here). If I were you and I were really determined to get a PhD in Statistics, I would prioritize Masters degrees, and maybe send out a few PhD applications if you can afford it. Then after getting the Masters, I would still target mainly schools in the 20-40 range, but apply to a few more highly ranked ones and see if you get lucky.

There are several factors that could be mitigating: - the prestige of your undergraduate institution and its reputation for grade deflation/academic rigor (if any). If you went to Caltech, MIT, or UChicago, etc., a GPA under 3.7 is likely to be more acceptable than a 4.0 from a regional state school. IIRC, one participant on this forum attended UChicago and got admitted to Berkeley Stats PhD with an overall GPA under 3.7 (but a GPA that was higher in the math classes). - your GPA in your major (whether or not that's higher than your overall GPA and what your major is), your GPA in math classes, and if your academic performance shows noticeable improvement over time. If your GPA is a 3.51 because of subpar performance your freshman year, that's one thing, and you can have one of your LOR writers emphasize the upward trend. If your major was physics or engineering or something and your major GPA was pretty good overall, you might still have a chance. Nevertheless, you might be out of the running for the very top tier PhD programs (if you want to do a PhD). But I don't think you're automatically out of the discussion for respectable mid-tier programs. If you could contextualize your profile better, we can give more targeted advice here.

No, I wouldn't bother. That score is fine, and it isn't so low that it would raise any red flags. Sometimes if you had a lighter math background, went to a relatively unknown school, or didn't get the best math grades, taking and scoring well on the Mathematics *Subject* GRE can help boost your application. But unless one of those things applies or unless you're aiming for one of the few programs that actually requires the Subject GRE, I wouldn't recommend even taking that.

If you could indicate the reputation of your undergrad, that would help a lot more in our assessments. But in my opinion, most of the schools in 10-20 are also far reaches for you. I think the most realistic range to target is 40-50, but you can try applying to a few higher ranked ones if you have the funds. Are you interested in academia or industry? For industry, it won't matter that much. And for academia, it's much more important that you have a good publication record and recommendation letters than where you got your PhD...

Assuming you have publications (either in press or submitted), a PhD from any of the well-regarded schools in Europe (Oxford, Cambridge, ETH Zurich, Warwick, etc.) would make you competitive in the academic job market in the U.S. and Canada. I'm not as certain about industry, but securing a job in academia in the U.S. or Canada with a PhD from a good European would typically not be a problem. Additionally, the reputation of the PhD advisor and publications are the most important thing. Someone who works with Peter Buhlmann or Sara van de Geer, for example, and publishes journal articles as a PhD student should have almost no difficulty landing a good postdoc in the U.S. I don't know much about admissions in Europe, but the PhD programs there contain no coursework, only research. So you would spend 3-4 years just doing research and would need to teach yourself all the material needed for your research (but every PhD student in the U.S. has to more-or-less teach themselves their research area/topic, and everyone has some guidance from their PhD supervisor in the beginning). I do get the impression that Statistics in Europe is quite theoretical in general, though, so they may prefer to admit students who have already taken classes like measure theoretic probability, advanced statistical inference, and more advanced theoretical courses (classes that would typically be part of the PhD curriculum in the U.S., but not part of the Masters curriculum here).