StatsG0d

I agree with @Stat Assistant Professor. Students have been pushing to modernize curricula, but it's difficult because professors are always concerned about prestige or rigor. Some topics I think should always be covered are:
Bayesian statistics (becoming more and more used in practice, even being picked up by CS people) Computation / simulation (preferably in C++ / Python and on Unix servers) Machine learning / nonparametric statistics (may be a buzz word, but it gets you jobs) Missing data (very common in practice) Some topics I think can be tossed out, that are typically required:
Measure theory (useful for many people, but not for all) Decision theory (hardly ever used in practice) Anything concerned with unbiased estimation (UMVUE, etc.--most practical estimators are biased so who cares) I do think UMP and UMPU tests are important, albeit boring, at least for biostatistics. Drug approval ultimately depends on having a significant p-value, so you def. want to have power.

I'm not sure if any Statistics program will specifically be a pipeline for federal jobs. I do know a few PhD alumni from different programs who have ended up at places like national labs (e.g. Los Alamos National Laboratory), as well as federal agencies and government-sponsored enterprise like the FDA, the NASS branch of the USDA, Department of Defense, and Freddie Mac. If you go to any Stat PhD program and you are an American citizen, then I don't think it matters a whole lot where you got your PhD. It is possible that your research area matters though. Some of the federal jobs require a "technical talk" as part of your interview, and if you have particular expertise in an area of interest to them, you could get hired just on that basis. For example, there used to be a professor at the department where I got my PhD who left academia to work as a Director of R&D at the NASS. I'm pretty sure this was largely because this professor's research focused a lot on spatial statistics and ecological/environmental applications.
I think maybe Biostatistics sends more alumni to certain types of federal jobs, e.g. at the FDA.

It's possible (and highly likely) that the other students had already taken the course (e.g., in undergrad). You definitely need to take real analysis for stats. You *might* be able to get away without taking it if you're doing biostats (although, this is becoming less common at the top 5-7 programs, as the field is becoming more competitive).
Even if you managed to get into a program without taking analysis, you would have wished that you'd taken it. Even in Casella-Berger level Math Stats, real analysis is very useful for making mathematically rigorous arguments / proofs. I feel like you (or anyone without real analysis) would struggle in a pure stats program without it.
Any/all of those courses will be useful when you reach the dissertation stage, but the reality is adcoms don't really care much about how many statistics courses are taken (unless they're mathematically rigorous courses e.g., linear models, probability theory, (martingale-based) survival analysis, etc.). If I'm on an adcom and I see that you've taken these stats courses, I'll think "OK, it's nice that they clearly have shown an interest in statistics, but how prepared are they to be successful in the program?" I'd look at the GRE and see a lower score relative to other applicants, and then think "well, perhaps this student had a lower GRE score, but has demonstrated mathematical maturity through courses." Then, when I see the lack of a single proof-based course on the profile, I would almost certainly reject the applicant. 
 
I think it's important for you to reflect deeply and see if you know what you're getting yourself into. If you are trying to avoid taking real analysis because you dislike theory, then I can assure you that you will not like doing a stats PhD, and you will burn out really quickly. The courses / qualifying exam is difficult even for those that have taken real analysis, and I truthfully can't imagine an individual doing well without it, especially relative to peers.
If you are more interested in the application of statistics, there are other fields you can consider that utilize advanced statistical methods (e.g., epidemiology, psychology, quantitative methods in the social sciences) without the need to dive into the theory. The purpose of a stats PhD is to make you equipped to develop your own methods.

I think you're selling yourself really short. First, I wouldn't bother applying to UCSD, TAMU, Rutgers, or Rice. NCSU doesn't have a PhD in biostatistics, although they do have a Biostatistics concentration in their Statistics department. Also, not sure if UW refers to Wisconsin or Washington.
You're competitive for any of the biostats programs in the top-5. In fact, I would be shocked if you didn't at least get into one of UNC or Michigan. I would say apply to all the top-7 biostats programs and maybe those Canadian schools if you're interested. I'd maybe add McGill if you're interested in precision medicine. I'd also add Berkeley if you're interested in causal inference.
It sort of depends. If you've taken all the qualifying exam courses, the department *might* let you take the qualifying exam the summer you arrive. Otherwise, they may force you to retake their versions of the courses and then take the qualifying exam at the end of the first year. It usually depends on both the department and the specific case.
If the qualifying exam is taken in the 2nd year (e.g., how it is at UNC), they'll let you skip the first year curriculum, but you'll have to take the 2nd year curriculum and take the qualifying exam the summer after your first year.

I agree if the OP meant stats departments then they should apply to those. I assumed they meant biostats 

I think you're selling yourself really short. First, I wouldn't bother applying to UCSD, TAMU, Rutgers, or Rice. NCSU doesn't have a PhD in biostatistics, although they do have a Biostatistics concentration in their Statistics department. Also, not sure if UW refers to Wisconsin or Washington.
You're competitive for any of the biostats programs in the top-5. In fact, I would be shocked if you didn't at least get into one of UNC or Michigan. I would say apply to all the top-7 biostats programs and maybe those Canadian schools if you're interested. I'd maybe add McGill if you're interested in precision medicine. I'd also add Berkeley if you're interested in causal inference.
It sort of depends. If you've taken all the qualifying exam courses, the department *might* let you take the qualifying exam the summer you arrive. Otherwise, they may force you to retake their versions of the courses and then take the qualifying exam at the end of the first year. It usually depends on both the department and the specific case.
If the qualifying exam is taken in the 2nd year (e.g., how it is at UNC), they'll let you skip the first year curriculum, but you'll have to take the 2nd year curriculum and take the qualifying exam the summer after your first year.

Harvard, Berkley, UW Stats all have at least 3 very top/rising star type people doing causal inference research. An important distinction you have to make is whether you want to do "classical" causal inference  (propensity scores, average treatment effects, instrumental variables, potential outcomes framework) or "modern" causal inference (dags,judea pearl causal discovery,  reinforcement learning, adaptive designs etc...). Both are pretty hot right now but the flavor of research is extremely different.
 

If you have the prerequisites, epi and econometrics are good disciplines for causal inference research as well.

UNC is pretty mathematically rigorous relative to its peers, and you will be taking courses alongside first-year PhD students who will have taken (likely) much more math than you. That said, there are many students who have the minimal math background (i.e., Calculus I-III and linear algebra) and are successful. There will be a master's exam that is only required for master's-level students, and if you pass the exam then it's smooth sailing to get the degree.
Also, note that UNC is on a pass/fail system, where the grades received are H (high pass), P (pass), L (low pass) and F (fail). Typically, <10% of students will get an H, almost all the rest of the students will get a P unless they did not do the work or bombed every single test, both of which are rare. I have never heard of anyone receiving an F. You can get two L's and still get the degree, but, again, I have seen very few students get L's.

I don't agree with this at all. Wake Forest is a very reputable school and there's a list of institutions that their Master's graduates end up attending, many of which are very prestigious. Not sure if you're trying to actually take a knock at Wake Forest in particular or if you were just oblivious to this fact.
I think the OP has a great chance at top-20 programs and a small but nonzero chance at a top-10 stats. I could see them getting into any/all of the top-5 biostats programs. Their mathematics knowledge is extremely deep--far deeper than the vast majority of domestic students. They have a letter writer with connects at the institutions in which they are applying. Got a perfect GRE Q and writing score. This is a really strong profile IMO.
The biggest problem is going to be that it seems the OP has quite specific research interests. I think it will be difficult to find a good program that aligns with these interests.
OP: I recommend you apply broadly (a couple in the top-10, most in the 11-30 range, some in the 30+ range to be "safe"). I do NOT advocate for speaking about your specific research interests in your statement of purpose, because I think if it's not a departmental interest they will be likely to reject you. Simply say you're interested in high dimensional statistics or something.

Parroting @StatsG0d point, I think you're really on the wrong forum. The people in this forum are fundamentally interested in statistical inference and probabilistic modeling. NYU DS (I have researched the department extensively, and even wrote a specific SOP for it and then I realized I wasn't a good fit after I realized how bad the SOP was) and what you seem to be interested in are more in developing computational tools that push the bounds of what is learnable. Rather than being concerned with proving consistency/convergence or statistical estimation problems they're more interested in solving problems like computational tractability, gradient zeroing, algorithmic correctness/efficiency, good representation for efficient information retrieval (See Dynamic Programming Algorithm for Chomsky Normal Form),methods for compressing neural network . Tbh for developing algorithms like EM and MCMC and even impactful NN work which is just optimization, proofs of convergence are extremely important in both fields and ya gotta be good at Analysis.
You should ask around whatever CS/Bioinformatics forums are out there. But to get into programs that attack these problems, you'd need demonstrated competency in CS topics like data-structures, systems programming, analysis of algorithms, numerical analysis. With your research background , which is on the weaker side for CS,  I think you'd need a good theoretical math background. If you're interested in it, I'd encourage you to apply, shoot for the stars man/gal. But if you want to do DL research, Statistics departments are not for you.
 
On a philosophical note that I hope you feel free to ignore as I don't know your entire situation: judging from the thread, it seems like you're seeing a PhD as a silver bullet for the existential pain of working in late capitalism. Unfortunately no matter what you do (yes, even most professors who aren't Michael Jordan or tibirashiani, and definitely most grad students) 80% of your time will be spent doing menial pretty frustrating work, but you have to find the other 20% to make it worth it. And even if the actual job all sucks there's almost always a silver lining in a job if you have  masters (pay which you mentioned, work life balance etc..). If your job is super interesting, it's probably going to have bad work-life balance and the contrapositive is also true. Having a lot of life suck is just unfortunately part of life and being happy is an explicit effort you have to make.
Not to say, you shouldn't try to change, but just having a PhD won't make things better, worse, harder, easier. It won't make you smarter or dumber, it'll just make things different. Seeing things like NYU DS PhD is an attractive solution because it seems so simple, do X get Y. But life doesn't work like that and having a PhD creates a whole host of new problems that you might not be happy dealing with if your primary motivation for a PhD is just that you hate your current job.
For context, I work as a datascientist at a Fortune 100 financial services company, and I hate it so much. Everyday when I wake up I curse Bill Gates for spawning Excel/Powerpoint from the 10th circle of hell. I have to use incredible amounts of MBA jargon, but the second I use the words "conditional on" the MBAs lose their minds. I can say with confidence that my job is probably worse than yours. The job tortured my very soul for a while, until I saw the finale of the office while slacking off from work:
 
I realized that although my entire job sucks, I have the work life balance to spend more time with my family, my aging dog, my girlfriend. I've gotten pretty decent at classical guitar and picked up a bunch of other stupid hobbies (e.g latte art and fishing). I realized that when I'm a graduate student drowning in qualifying exams and research, I'll definitely miss this job that I currently hate.
Sorry, this probably wasn't helpful, but I just want to warn that a PhD shouldn't be viewed as a solution to a problem. It's a luxury and a privileged that you should deeply want.

IMO, what will boost your application the most is none of the above. If you are able to, it's better to take upper-level proof-based math courses, even at the undergrad level. The biggest doubt of your application is your math ability. Consider taking courses like (in no particular order)
Number theory Abstract Algebra Complex analysis

This is a very interesting and atypical profile. First, if you obtained your MS at least 2 years before you will be submitting applications to PhD programs, I highly recommend applying for the NSF GRFP and writing about that in your statement of purpose. Not a deal breaker if you can't do it, but it will tell admissions committees that you're very serious about research and have thought deeply. Based on your background and credentials, I think you would have a good shot.
Now, although you did not do well in undergrad, you have very good grades in graduate level stats courses, which is great. The B+ in multivariable calc might raise a suspicion, but can be overcome by a good grade in analysis. I recommend maybe also taking real analysis II (if you're only applying to stats departments) to show adcoms that your math ability has improved.
I think you definitely have a shot outside the top-20. I think you'll have a better chance in biostatistics programs, which I feel are more likely to admit students with atypical backgrounds / interesting profiles (likely due to the proximity to public health). I feel like with good grades in high level math courses, you have a good shot at biostats programs like UNC, Michigan, Emory, Minnesota, and a really good chance at programs like Pitt, Vanderbilt, etc.
To ease any concerns, hardly anyone has biostats "experience" prior to applying. All the areas you mentioned save for operations research have important and active biostatistics research areas. I'll summarize below, and leave it to you to decide if you're interested in them:
Statistical Learning - Precision/Personalized Medicine Develop / utilizing ML algorithms to find the right treatment at the right time for the right person Typically concerned with proving under some assumptions the resulting treatment rule is optimal High Dimensional Data Analysis - variable selection / genomics High dimensional data arises naturally in biostatistics / public health (e.g., genomic data, electronic health records (EHR) Outlier analysis Can't give a specific example, but outliers occur often in biostatistics (and in pretty much any data application)

It's probably much more difficult as an international student, but FWIW I did an undergraduate degree in Economics and I was admitted to nearly all the schools I applied to in the 10-20 range.

Undergrad Institution: QS Asian U top 100
Major(s): Statistics
GPA: 3.78 after conversion Type of Student: International male

GRE General Test:
Q: 169 (94)
V: 152 (53)
W: 4 (55)

TOEFL Score: 100=25+25+24+26

Grad Institution: Same with undergrad Major: Statistics
GPA: not good
  Programs Applying: Statistics/Biostatistics Ph.D. programs   Research Experience: one methodology publication in CSAM, two application publications in domestic journals (text analysis and GLM), one theory paper under review
Awards/Honors/Recognitions: honorable mention for a poster presentation
Pertinent Activities or Jobs: TA at grad school, part-time lecturer at a corporation Letters of Recommendation: two from profs with whom I worked on papers, one from advisor (strongest in terms of the personal relationships) Math/Statistics Grades:  Calculus I & II, Linear Algebra, Mathematical Statistics I & II, real analyses, 20 statistics major courses in total (took about 95% of available major courses until graduation), database, digital logic  
OSU - Statistics / Admitted in late Mar / Accepted MSU - Statistics / Admitted in early Mar / Declined U Iowa - Statistics / Admitted in early Mar / Declined U of SC - Statistics / Admitted in mid Feb / Declined UCSB - Statistics / Waitlisted - Admitted on Apr 14 / Declined   CSU, UCR - Withdrawn   ... AND 15 REJECTIONS   I'm pretty happy in that I got into one of my very best options even though I did not earn MS in prestigious US programs and get recommendations from well-known American professors. I can say that the profile evaluations and program recommendations people make in this forum are quite accurate and helpful even for minority in the applicant population like me. @bayessays@Stat Assistant Professor@StatsG0d I truly appreciate your help and advices!!

Undergrad Institution: Asia QS rank around 150 Major: Humanities  GPA: 3.61   Grad Institution in the same school MA in Economics  GPA: 3.9 Type of Student: International Male

GRE General Test:
Q: 169 
V: 155
W: 3.5 
GRE Subject Test in Mathematics:
X
TOEFL Score: (R28/28/S21/W22) but most programs waived the condition.
Grad Institution: MS Statistics in US (Ranked 16~25) Concentration: 
GPA: 3.78
  Programs Applying: (Statistics PhD only)   Research Experience: Research experience in labor economics. I worked for a year, did a presentation but did not published the paper. Also I did a small project in a graduate statistics course.
Pertinent Activities or Jobs: (TA job in the previous program in Asia) Letters of Recommendation:   One from Economics professor I worked with for my research and I did TA for him for two years. Two from Statistics Professors, and the last one was from the professor who taught me Honor Analysis 1. Math/Statistics Grades:  1) I did not took calculus sequence officially, but I learned them through courses like Mathematics in Economics and self-study .   2) Advanced mathematics for Engineers(Linear Algebra),  A, previous program in Asia.   3)Real Analysis(measure theory), B- , previous program in Asia.    4)Functional Analysis A, previous program in Asia.   (Do you see something strange?, I took measure theory and a functional analysis without a solid background in undergrad level analysis, and now I am pretty sure I have to study again those subjects since I feel my background in analysis is much more solid then before) . 5) Advanced Calculus 1, A, US, almost the first course I seriously started to study real analysis.  6) Honor Analysis 1, B-> A-(I retook it, in the back then I didn't want to quit so did not choose the option "W", US. 7) Currently, I am taking Honor Analysis 2, US. ?Casella Burger Theory of probability and Statistics sequence(A,A), US.   Also, I took bunch of courses here and there such as Statistics in Economics(graduate) A, Asia, Econometrics(graduate), A, Asia, Micro-Econometrics(graduate),A, Asia, Regression course(graduate) A-,US, Design of experiments(graduate), A-, US.   Undergrad courses or master level courses in US: Intro to Machine learning(P), Time Series(A-), Multivariate Analysis(A), Statistical Computing(A), Regression Course(A), , Undergrad Probability theory(A), and maybe some more in the previous program. 
Applying to Where:  PhD Statistics programs only I applied to 17 Statistics programs ranked 20~55 in USnews.  Rest of the programs beside the three below, I got rejected.
School - University of Iowa / Admitted/ 2.28 / Accepted     (But they put me on the waitlist for funding maybe because I was not very enthusiastic responding to their program.)        
School - Colorado State University  / Admitted/ 4.13 / Accepted 
School - Rutgers University / Admitted/ 4.12 / Accepted   I am still hesitating about posting which could be easily recognized by people but I am indebted to people in here, so I am posting mine out of the responsibility and in a hope to improve information asymmetry between applicants and schools. I wish my profile and the results can give hope to future applicants especially those who plan to change their fields.   Firstly, I want to stress that if you start late with a weak mathematical background, you should never be hasty. Otherwise, you might end up spending much more time to study those prerequisite courses. Take a step by step, there is a reason why curriculums were constructed as the way they are.   For applicants who do not have a super strong profile, I suggest try to secure more than three letters. One reason is that, in my opinion, you should not be so sure about which professor would write a best letter for you especially when you are not the #1 in your department. I saw many people saying they've secured the best letters, but seeing their application results, I doubt it. On the other hand, even if you submit different combination of letters, they will probably have about the same power because a strong letter comes from a strong reason why they should recommend you. Still, I would suggest to secure more than three letters because you do not know which schools have a connection with your recommenders and this may important than you think.    For the international students, when you plan to go master program in U.S. hoping for an opportunity, think twice. If you get admitted into top master program, I guess the risk becomes smaller but if you are not, then the risk becomes exponentially larger. If you were not competitive in your own country, think twice which factors would make you a different person after a year or two in U.S. Also, be aware that you may not have many opportunities for RA or TA even comparing to undergrad students because they have different sources which are only available to undergrad students and the resources for grad students go to PhD Students. So, ambitious international students who have already proved your competitiveness in your country but lacking strong letters are encouraged to apply for master program in U.S. If it is not the case, you should know about the risk before starting your journey, I have no intention to discourage anyone. This is more like I would have wanted to hear before I start this process. Actually similar stories apply to domestic applicants as well.    Lastly, apply to as many schools as you are allowed to. The margin cost of applying to extra one program gets smaller by each iteration. So do not think about the cost of applications, think about the real cost that you have payed including your youth.    I will not list names, but I really appreciate people who shared their knowledge and experiences in this forum.      

Yeah, I agree. @cinets you should use the template utilized by others on this forum and give us more details on your profile.
However, I think the B+ in multivariable calc and relatively shallow math background in general will be a detriment to your application, even if you've published a good paper. You should consider taking Real Analysis II and getting a really good grade in it. Consider taking other proof-based courses as well. 

Your list looks very reasonable to apply to with your profile. I think BU and Pitt are relatively safe choices for your profile, and it wouldn't hurt to add another school like MD Anderson or University of Minnesota Biostatistics (i.e. in roughly the same tier as Columbia and UPenn Perelman).

>6 years is crazy! most Biostatistics students finish in less than 5 years.

It is true. The audience of genetics/genomics methods is different from general biostatistics. Top genetics/genomics methods papers are typically published in journals like Nature Methods, Nature Communications, Genome Biology, and Genome Research etc.

Goncalo Abecasis spends most of his time in industry now. Those high citation papers are large consortium applied papers that aren't necessarily good for PhD students who need to get methods papers for graduation. I have heard some students struggled to graduate. 

Even if you were to do well in your Masters program, I would consider UC Berkeley, Columbia, and Yale to be completely unrealistic. You have too many B's, and the competition for these schools is very stiff. Some of these schools only accept very few domestic applicants to begin with, and I'm afraid you won't be able to compete against applicants from Ivy schools, Stanford, UChicago, MIT, etc. with higher GPAs and possibly some solid research experience. 
 UC-Davis, UCLA, UC Irvine, and UT-Austin are reaches as well, IMO. It seems as though the UC schools are all very competitive, regardless of their rank (except for maybe UC-Riverside), because of their desirable locations. But I'm not sure how open UC-Davis would be to accepting their own Masters students as long as you perform well in their program, though -- that might be something to look into.
I would say that in order to be competitive for PhD programs, you have to get all A's in your Masters program, especially since you got a B in a graduate Statistics course. Definitely also take a full year of analysis and get A's to make up for your B in undergrad and possibly one or two other advanced math class (e.g. proof-based linear algebra) to show that you can succeed in math-heavy courses. The second year of a Statistics PhD program is pretty theoretical for the most part. If you do well in your Masters, you might be able to get into a program like TAMU or Iowa State. However, the most realistic schools would probably be those in the range of 37-80 of the USNWR rankings (i.e. those ranked below Yale). For your profile, I would consider TAMU and ISU to be the upper end of the schools you should be applying to for Statistics PhDs. 

Also, one factor you should consider is that Han Liu may no longer take students at Northwestern. Or, in other words, he hasn't taken any new students since he left Princeton.

I agree there's not really a huge difference between the two programs. Historically, I think Wisconsin has been stronger, but they've lost a lot of good faculty over the last couple of decades and allegedly had funding issues recently. Between these two schools, I would say go wherever you feel like you'd be happier.
I think the biggest strength PSU has over Wisconsin (and many other schools) is the large amount of electives that you can take (as @bayessays alluded to). The student culture at PSU is awesome as well (highly collaborative, not very competitive), although I have never been to Wisconsin so I can't comment on theirs.
FWIW: Madison is probably the more interesting place to live as PSU is right in the middle of nowhere (which is why I opted not to go there, despite really liking the department culture/atmosphere). Madison, WI has about 250,000 people vs. about 45,000 in State College, PA. State College is about 2.5 hours to Pittsburgh and 3.5 hours to Philadelphia. If you don't really care much for city life, it could probably be a great place as there's a lot of hiking around the area.

These two programs aren't really on the same level at all. The only reason I can think to go to NU over TAMU is if you have very strong location preferences. Otherwise, I don't really see a single advantage NU has over TAMU.