statsguy

I would probably not mention it. Say something along the lines of "despite struggling to find my way in my first two years of college, I overcame adversity and realized I need to take a different approach. This is why I did far better in the second half, despite having a tougher workload."  Obviously you'll need to wordsmith this and make it sound polished and convincing, but you get the drift."

You may or may not find the position you're looking for. To be frank, a lot of problems in industry just don't need the latest cutting-edge methods or complicated simulations. There is a reason why tools like linear regression and the two sample t-test have been around forever - they are quick and easy, and they work. 
Many years ago I was talking to a PhD data scientist at a FAANG company who was doing A/B testing. I'm pretty well-versed in experimental design and assumed they would be using the latest and greatest computer-general designs. Turns out their bread-and-butter technique was the full two-level factorial design analyzed using standard ANOVA, something a competent undergraduate could probably do.  This was probably 7 years ago so things may have changed... but maybe not because they seemed really happy with their results.
Your best bet is to learn as much coding as possible (R + Python) in your free time. A PhD in Stats would be good although it's probably going to be a grind. I'm not sure how much your MS in Biostats will get you if you start fresh at a PhD Stats program. You'll also have to consider 5+ years at low pay, no benefits like 401k, missed raises/promotions you would've gotten in industry... but that's a personal decision. Financially the PhD may not be the clear winner at all in your case.
Another path you can consider is perhaps sticking it out a few years, and maybe getting an MBA later? If the management track would ever be of interest to you. 
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

It unfortunately doesn't matter whether the PhD is actually "needed" from a knowledge/proficiency point of view - pharma companies want PhDs whether we agree with it or not. Pharma has a lot of money to spend and very large R&D budgets, they are more than willing to pay a premium for PhD statisticians. Not only PhD statisticians, but they hire tons of PhD scientists as well for the senior R&D roles.
Also there are tons of roles in Pharma - I know one guy who works in optimization and logistics on the manufacturing side of a pharma company. Him and his crew of  statisticians and industrial engineers do lots of simulation work, use R/Python, and come across lots of "non standard" problems that a PhD would be well-equipped to solve. I know another guy with a PhD in Statistics who is a director of business analytics and data science at a smaller pharma company. Not everyone works on clinical trials, regulatory submissions, etc. 
Having been in industry my entire career (startup that later went public, then data science at a large tech company, now middle management at a manufacturing conglomerate) with a PhD from a top-15 department, the PhD has made life so, so much easier. I have seen a few people with Masters excel and move up, but there is definitely more of a ceiling. And on at least one occasion I've come across someone telling me that they want a PhD to fill a role because it simply "looks better". 
If you're young and don't have kids, it may be worth biting the bullet and doing the PhD. Good luck!
 
 

Is there really a “need” though for the PhD in Pharma from an objective sense? A ton of the biostat work in pharma is not even actually statistical stuff, its mostly regulatory and documentation related. Pharma especially is super conservative and even uses SAS, which is the opposite of cutting edge. 
Having worked as an MS stat in biotech I hate it and that is why I’m looking at a PhD. But the PhDs Biostat I see in this area aren’t exactly doing the cutting edge work either. They are writing the same boring protocols and validation reports I am. Its not data analysis focused at all, as opposed to DS in tech. 

Pharma has a ton of PhD-only positions. 
I went to a top-15 university, and while there were some very solid MS students, you simply cannot compare a <= 2year education that was mostly applied coursework to a >= 5year PhD that had rigorous theory classes, lots of applied classes and projects, and you had to know your stuff well enough to pass a difficult written qual, prelim oral exam, and had to solve an open problem and write 100+ pages about it. PhD students also had more time to master R, read literature, learn coding, attend conferences, TA/teach various courses, and had a greater depth of knowledge of the field in general. In some cases the PhD is also proof that someone can "stick it out", put up with a lot of BS, etc. as well. 
This is not a knock on MS students, and for some positions, we actually prefer the MS degree. We often have positions that accept both MS/PhD applicants and we've occasionally chosen an MS that was a better fit.

I've worked in industry for my entire career, at a startup that eventually went public, at a well-known tech company, and now in R&D middle management at a more "old school" company near where I grew up.
Higher-ranked program helps a bit because (1) they're often doing research in hot areas that translates well to industry work (places like Stanford) (2) better connections when looking for an internship or job and (3) the "wow" factor of a big-name program - you'll get some extra attention, but you're not a shoe-in by any means. None of these are insurmountable if you come from a lower ranked school.
Biggest things that'll help in the job search are internships, dissertation topic, connections, and personality/likeability. If we're looking for a MS/PhD statistician to help an army of engineers with experimental design, statistical process control, and data analysis, we'd much rather take someone who did a lot of applied work at a school ranked #42 and had an internship at a place like Procter&Gamble, than take someone who spent their entire PhD working a theoretical topic in stochastic processes at school ranked #9. We've even hired Master's over PhDs if they were a better fit.
 
 

It's hard to go wrong with Stanford. Assuming you actually got in to Stanford... go to Stanford. 

I think Harvard is a much better biostats program than Berkeley biostats.  I think Berkeley stats is a better program than Harvard stats.  If you are dead set on biostats go to Harvard.  However getting a degree in stats gives you more flexibility.  Depending on what you want to do, going to Berkeley gives you more options for the future.

Many don't do research first year, but those that start first year (and stick with their topic/advisor), have a large advantage. The Penn state offer facilitates starting research year 1.
Another thing to consider is that if you're getting such a good first-year offer from Penn State, that probably means they really want you to attend and you're a "top tier" applicant. If you continue to show promise, chances are you'll get some more perqs down the road (fellowships, easy teaching assignments if you get a TA, summer funding, course waivers etc.). The department I went to (top 15 in Stats) had 1 student that got a first-year fellowship. This student was usually treat very, very well even in subsequent years...

I've seen a few cases where it happened.
1. One guy worked at a well-known tech company in a research-type role, and part of his job was publishing and presenting papers, so he got a TT job the first year he applied. It was a so-so state school with a combined Math/Stats department that I believe offers an MS in Stats. Not exactly a "prestigious" position but it was near where him and his wife grew up, so it was a no-brainer for him.
2. Another guy worked in a run of the mill R&D data analyst type role, got hired as a department "instructor" at a local university (basically a teaching-only position, usually 1-3 year contracts), did research on his own, published a ground-breaking paper as well as a couple other solid papers in his free time, got a TT job a year or two later.
3. I've heard of at least one person going back to do a post-doc and eventually getting a tenure track job.
It's very impractical to do this. I agree with @Stat Assistant Professor that you'll need some sort of springboard to get back into academia. Keep in mind also, as an example: going from making $150k/year in an area like Chicagoland, with great benefits & 40-50 hours/week workload, to a post-doc (requiring a move) making $50k/year working 60-80 hours/week, and then getting rehired at $80k/year in a publish or perish environment (in a possibly awful location) would be an enormous financial and QOL hit, all for the possibility of tenure in 5-7 years. If you have kids, or a spouse that's unwilling or unable to move, it may be totally impossible to make this work. There's a reason why hardly anyone does this.

The department hooked you up with a fellowship, specifically for disadvantaged students. That's easily worth 10-15hours/week. Most of us had teaching assistantships and they were really annoying. Grading, teaching labs, answering student emails everyday, office hours... it was a major distraction. Perhaps they see potential in you and think you could use the extra 10-15/hours week to catch up? To say they don't support you at all seems like a stretch.
The women in our department were actually less pressured to "take" service work (not that we had a choice) and more likely to get a fellowship that didn't require teaching. And to be blunt, most of the US women in our department were from fairly affluent families, went to good expensive private colleges. At the same time, we had a guy who grew up in a trailer in rural Alabama, went to a weak state school, and ended up being one of the department legends since he absolutely crushed research and got a really prestigious post-doc and later TT position at a top-15 school (he recently got promoted to Asoc prof). He struggled big time his first year, failed the qual the first time, but with 70-80 hours/week year-round, he shined. Yet he had none of the advantages a woman from a middle-class family has like women-only fellowships or affirmative action.
We can theorize all we want but practically speaking, it's not going to change your situation. Spending time pondering the fairness of the situation is fine, but ultimately it's not going to get you the PhD. My suggestion is to hunker down and study hard, hard, hard. 60-80hours/week minimum. Study during winter and spring break. Take a few weeks off in the summer and then hit the books again. The PhD requires a high-degree of self-sufficiency. When you do your dissertation/research down the road, you'll be pretty much on your own. Take up a hobby that you can do for 1 hour/day like running, walking, cycling, cooking etc. to burn off some steam.
One final piece of advice would be to wait before making too much noise. Give it at least one full year. Things may start to "come together". Causing too much noise (unless of course, you have a clear example of sexism/anti-woman bias in the department) is not going to win you any friends among the faculty. Right now you're speculating.
I'm done with this thread- best of luck!
 

In your case, I would find a department that has diverse research. I was in your same position, so I chose the department that (1) had a great culture and location and (2) had someone doing research in many diverse topics with little intersection. That really gave me a lot to choose from when year 3 came around. 
Other points to consider:
Have any departments given you a research assistantship years 1-2? If so, that's pretty substantial. When I was a year 1-2 teaching assistant, I easily spent 10-15 hours/week teaching labs, grading, office hours, answering emails, etc. This was a major time suck and distraction, although I kind of enjoyed it. While I generally don't recommend looking at financial aid packages since it's a short-term thing (as long as they are at or above subsistence level), if one package is substantially higher than the others, you can save the excess money in a Roth IRA over 5-6 years which could easily be worth six figures when you retire. I really regret not putting at least $1-2k in a Roth IRA each year when I was a PhD student.  Location matters, if you're into "culture" and sports and whatnot. I wasn't. All I needed was a gym, a running trail, a liquor store, and a dive bar. Which meant I would be happy anywhere. Some people need to be near museums, theatres, sports games, or whatever. Also consider weather - if you're a lifelong Florida resident, going to the University of Wisconsin, Michigan, or Minnesota could make for some very rough and depressing winters. If you're planning on going into industry, you may want to consider a university that's near a lot of companies. This will make getting internships slightly easier since there's no relocation involved. You may want to consider the culture of the departments as well. Some departments are super social and fun. Others are super lame.  Best of luck to you.

In your case, I would find a department that has diverse research. I was in your same position, so I chose the department that (1) had a great culture and location and (2) had someone doing research in many diverse topics with little intersection. That really gave me a lot to choose from when year 3 came around. 
Other points to consider:
Have any departments given you a research assistantship years 1-2? If so, that's pretty substantial. When I was a year 1-2 teaching assistant, I easily spent 10-15 hours/week teaching labs, grading, office hours, answering emails, etc. This was a major time suck and distraction, although I kind of enjoyed it. While I generally don't recommend looking at financial aid packages since it's a short-term thing (as long as they are at or above subsistence level), if one package is substantially higher than the others, you can save the excess money in a Roth IRA over 5-6 years which could easily be worth six figures when you retire. I really regret not putting at least $1-2k in a Roth IRA each year when I was a PhD student.  Location matters, if you're into "culture" and sports and whatnot. I wasn't. All I needed was a gym, a running trail, a liquor store, and a dive bar. Which meant I would be happy anywhere. Some people need to be near museums, theatres, sports games, or whatever. Also consider weather - if you're a lifelong Florida resident, going to the University of Wisconsin, Michigan, or Minnesota could make for some very rough and depressing winters. If you're planning on going into industry, you may want to consider a university that's near a lot of companies. This will make getting internships slightly easier since there's no relocation involved. You may want to consider the culture of the departments as well. Some departments are super social and fun. Others are super lame.  Best of luck to you.

Assistant professor advisors have a lot of advantages. They want to get tenure, which means they are super-motivated to publish. Advising a student that lands a good TT job looks really good for their own tenure case as well. When I was a 3rd-5th year grad student, I felt I could really relate to the new assistant profs that we hired on a personal level. The student/advisor line was much blurrier.  
A few disadvantages: less name brand recognition, less experience, and possibly fewer connections. When I got my PhD, the only faculty who left our department in those 5 years was an assistant professor - not sure if that's an overall trend (assistant profs more likely to move than associate or full profs). Having to deal with an advisor that moves institutions is a headache but not insurmountable.

I went to a top 15 department and found the culture to be great. Everyone got along with each other (aside from a few minor riffs here and there), and we even organized events like department happy hours once in a while. We'd collaborate on homework years 1-2, study for the written quals together, etc.
By year 3 everyone went their separate ways with research but we all still remained friendly with one another, helped each other do mock prelim oral exams, mock dissertation defense presentations, etc. 
Sure, there was maybe some element of competition but at the end of the day we were ultimately competing against ourselves. Aside from 2 very strong candidates, the intersection between academic job applications was minimal as well.
Great experience overall where I went. Some departments may have a culture that could be viewed as "lame" or boring (i.e. everyone does their own thing and goes home, little interaction among students) but I haven't heard of any departments where the culture is super toxic and ultra-competitive.

I should qualify as well that if you're aiming to get a job at (say) Stanford or Harvard or one of those very elite schools, then your chances of doing that coming from a "lower" ranked program are probably slim, unless you're seriously amazing (very productive, tons of top publications, etc.). However, pedigree should not preclude you from getting an academic job at a fairly good school nonetheless. Even the vast majority of PhD graduates from the "elite" schools who go into academia will end up at flagship and public universities (there are only so many jobs at the "elite" schools, after all).

I went to a top 15 department and found the culture to be great. Everyone got along with each other (aside from a few minor riffs here and there), and we even organized events like department happy hours once in a while. We'd collaborate on homework years 1-2, study for the written quals together, etc.
By year 3 everyone went their separate ways with research but we all still remained friendly with one another, helped each other do mock prelim oral exams, mock dissertation defense presentations, etc. 
Sure, there was maybe some element of competition but at the end of the day we were ultimately competing against ourselves. Aside from 2 very strong candidates, the intersection between academic job applications was minimal as well.
Great experience overall where I went. Some departments may have a culture that could be viewed as "lame" or boring (i.e. everyone does their own thing and goes home, little interaction among students) but I haven't heard of any departments where the culture is super toxic and ultra-competitive.

Agree bigtime.
The most important factor for TT jobs is research. Having a good advisor helps - by good I mean one that publishes a lot and is at the forefront of his/her research, sets you up well to do research on your own going forward, and has some name recognition. Higher-ranked departments tend to have more good advisors. But even lower/middle ranked departments will have a few solid, well-known guys. 
Ultimately, someone super-motivated and ultra-hard working can overcome the setbacks of being at a lower-ranked department.

I was admitted to a very good program years 10+ years ago. Admissions were less competitive then. Before the fall semester, I emailed all the profs and for their syllabus so I could see what topics they would cover. I immediately recognized I had a few weaknesses:
I only took one semester of linear algebra way back freshman year of undergrad, and I didn't do particularly well I didn't have a good foundation in theory in statistics I realized it was going to be rough. So I bought a Linear Algebra textbook and Casella/Berger and studied for 4-6 hours/day from June-August prior to starting the PhD. I watched online lectures, did problem sets, etc. This gave me an enormous boost my first year in the PhD program and made the qualifying exam a piece of cake down the road.
The point I'm making is that you've gotta get on this yourself. The profs I had in my first and second year couldn't care less if someone was struggling in their coursework - they cared about cranking out publications and getting their PhD candidates competitive for academic jobs. When we did the written quals, graders didn't have the names of the students on the exam paper, and each of the 8 problems was graded by a different faculty member to wash out any biases. There was no affirmative action on the written qual.
Our cohort started at 6/4 men/women. 3 didn't make it and coincidentally they were all women (2 American and 1 Chinese). The Chinese woman had mental health issues while 1 American woman failed the written qual 2 times and the other never got into the groove of research and left before her oral prelim.
I can say this has been my experience as well. The gap between US and international students narrowed bigly by year 3 and when looking at academic job placements in our cohort, the guy that got the "best" academic job was from the US, and he failed the qualifying exam the first time he took it.

How "low" are you talking? Fwiw, I went to a PhD program ranked ~40 in USNWR, and we have placed PhD grads in TT faculty positions at Duke, University of Minnesota, UT Austin, etc. And I have also seen people who got their PhDs from Baylor, University of Cincinnati, and University of Illinois at Chicago (*not* UIUC) get TT jobs at Texas A&M, University of Florida, and Iowa State.  
It's not *just* about where you get your PhD. For example, Dave Dunson has a PhD from Emory (a very solid biostats program but not a Stanford/Berkeley/Harvard), and Michael I. Jordan (considered one of the top researchers in statistics/ML) has a PhD in cognitive science from UCSD. Both of these guys are extremely renowned in the statistics field.  I can also think of other outstanding researchers who don't have PhDs from "top" schools who have done quite well in academia.
I don't want to dismiss rankings completely, but pedigree really is only one factor (and byfar not the most important one). Hiring committees *really* care about your past publication record, your future potential, your postdoc experience (a very productive postdoc at a prestigious institution can help you a lot), your letters of recommendation, your PhD advisor and influential scholars who can vouch for you, your teaching experience, etc. These are all things that are taken into account for academic hiring.