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."

Studying data science on the side should be a great way to satisfy you. Pre-covid there was a Python and ML group I'd attend occasionally if the talks looked interesting or there was a guest speaker that I wanted to meet. In fact, why not start up an R or Python package? You can start slowly, and contribute a piece at a time in your spare time. There are tons of cutting-edge tools that only exist as fragmented C++ code, if that... you'd be doing the world a great service, and you'd have something to put on your CV. I agree with those that say at the end of the day, it's just a job. You're in biotech with an MS, that means you're probably making a minimum of $80k/year working 40 hours a week doing easy problems. Paid vacation, 401k match, health insurance as well... This is based on what I was seeing 8+ years ago. Assistant professors at the top-15 program where I graduated from started at $78k/year when I graduated some years back, and they were easily working 60-70 hours/week in their quest for tenure. They were incredibly stressed and not all of their time was spent doing cutting edge stuff. They had to teach, serve on committees, referee papers, advise undergrads and MS students... I only published a few formal academic papers and I found the process to be a grind. Only about 20-30% of the time was spent on actually tinkering around and developing the methods. The rest of the time was spent on reading references, writing a lot, waiting for simulations to run, debugging simulations, responding to referee reports... not that fun IMO unless you're a professor with tons of grad students to do the grunt work for you. I now work at middle management at a manufacturing conglomerate and oversee a lot of statisticians and engineers doing applied statistics (design of experiments, statistical process control, process optimization, etc.). I have tons of time to spend with my kids and wife. I have time to train for half and full marathons. Surf Reddit. Read and post on several forums. Financially I never thought I'd be where I am now some 10-ish years after graduating. We bought our house in the midwest a few years back and are on track to pay it off next year. I'm perfectly okay with my job occasionally sucking (e.g. 4-hour Zoom meetings) because life is great outside of 8-4:30PM.

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.

I don't disagree that a Harvard Biostats PhD will be able to find a good job. My point is that if someone is dead-set on going into industry into data science, why bother with Biostats, even if it's a good department? I'd rather go to a lower ranked Stats department, do a dissertation in some sort of ML or AI or other hot topic that is computation heavy, take relevant electives (e.g. NOT things like survival analysis), do an internship (these help immensely), and go from there. In fact, I know someone who did a Biostat PostDoc at Hopkins, flopped on the academic market, and took a job at Google. So it's possible. But I also know of data scientists at Netflix, Amazon, who graduated from >35 Stats departments and had no issues finding great jobs. Going the Biostat route just seems unnecessarily harder. And even if you do get a sweet DS positions, you'll probably have some catching up to do.

On numerous occasions over the past 10 years I've seen the stereotype that Biostats = clinical trials + stats lite. It's unfortunate, and I personally don't agree with it, but it is what it is. 10+ years ago when I graduated with a Stats PhD, the industry-bound Biostats PhDs at my university all went to companies like Baxter, Medtronic, Boston Scientific, Merck etc. while the industry-bound Stats PhDs went to a very wide variety of companies - Pfizer, the IRS, Google, Amazon, and a startup (me). The coursework was also not the same, at least back then. Stats PhD had an extra year of theory, and the applied courses were much more general, with a slant towards machine learning, advanced regression, etc. The Biostats courses were much more specialized - things like longitudinal data analysis, survival analysis, etc. In fact, even categorical data analysis at that time was only offered in the Biostats department.

I would disagree with this based on my 10+ years of industry experience working at three fairly disjoint companies. Unless you're going into something hyper-specialized (e.g. genomics) a Stats PhD will generally be the better option.

You're overthinking this. How many positions have you had since you got your MS? Just because you got stuck doing gruntwork in a biostat position doesn't mean industry is all SAS and one-sample t-tests. When I was at a startup, our team worked around the clock to write algorithms to predict outcomes involving cancer diagnoses. Lots of R, Python, C/C++, etc. Based on what I later saw, most of the methods our team developed internally would blow away anything that was coming out of academia. But we were paid to help develop cancer screening methods that would ultimately save lives, not publish papers in journals whose audiences are 99% academics. Plus the whole thing about NDAs and confidentiality. When I worked at an established tech company, no one was spending their days writing SAS code to do one-way ANOVAs. The data scientists were working on interesting, novel problems and could use whatever tools they wanted. R&D budgets were large so if you wanted the latest Mac + expensive commercial software, it was all good. Lots of Linux and a handful of Windows users as well. Now at my manufacturing conglomerate, engineers use commercial software like Minitab or Design-Expert, while MS/PhD Statisticians use R or JMP. And FWIW, I highly suggest choosing the Stats route if you have the choice between a Stats PhD or Biostats PhD, even if that means having to take an extra semester or two of theory. You will be better off.

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!

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'd only go to UCLA if Michigan winters were an absolute-dealbreaker for you. But since you applied to UMIch I assume you took that into account. To conclude, Michigan all the way.

This was circa 2006, at a "teaching" undergraduate school where 2 (maybe 3) of the professors were from NC SAState. Times have since changed, fortunately. Edit: adding that when I was a PhD student, I took a Biostat elective that used SAS (although the instructor allowed R). And this was a very-high ranked Biostat department around 2011 ish? It's not totally unreasonable, as SAS is still widely-used in industry even to this day, which is where a good chunk of Biostat PhDs end up going. And at least at the time, SAS did some things better (e.g. anything involving mixed models) than the two major R options at the time (lme4, nlme). Pretty sure it took me nearly a full day just to acquire and install SAS.

On a somewhat unrelated note, spend some time learning R, Python, SAS, Julia or whatever the dominant language is in your department (probably R) - it will make life a lot easier. If you're burned out from studying theory, mess around with programming. Watch vids, do tutorials, make your own package from scratch. My first semester applied class assignments took probably 2x as long because I had to learn new concepts and also had to mess around with implementing it in R. Our undergrad only used SAS so it was a rough few months transitioning to R. I really regret not spending a few weeks over the summer to get a head start when I had a ton of free time.

Sounds like NC State wouldn't be a bad option for you. I would ignore the stiped amounts (as long as they are enough to sustain you), but no teaching is a decent perq. The area around NC State (RTP) has lots of internship opportunities that won't require a move (a major plus), and many industry job opportunities if you don't go into academia. Don't underestimate the effects of winters if you're not used to them - I went to a school that had long, cold winters with very little sunlight in the winter, and several students struggled to adjust.