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Big tech data scientist vs. big pharma biostatistician


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I'm a 5th year phd student studying biostatistics, expecting to graduate this year. I am now facing two options for choosing a career in industry. One is a data scientist position in a tech company and the other is biostatistician at a big pharma. These two are very different options with different pros and cons. I'll share some of my personal understanding of these two options. I think people already talk enough about the pros in tech (money, large datasets, new methods, etc), so I'll talk more about why I think pharma might also be a good choice to think over tech. 

It seems that pharma really needs biostats people to run the clinical trials. The work itself may not look exciting in the beginning. It involves many routine work, such as writing SAS, SAP, meetings with different teams, etc. In general, you need learn different components of clinical trials, not just the technical/statistics parts. In fact, there may not be many fancy modern stats methods involved in clinical trials, due to FDA regulation and other reasons. I believe the key part is really to understand the whole picture of clinical trials and get experiences over the years to prepare yourself for the potential of leading a trial. Also, there might be many regulatory writing involved. 

I think one advantage of being a biostats at pharma is that this career path is clear. You have people ahead of you showing you the examples of how to be successful here. And you can see where you are 10-20 years later. Say if I am at the age of 45, I might find being a biostats/associate director (AD)/director (depending on how much you have promoted) has easier lives than being a data scientist at competitive tech companies with many young talents. I just feel that pharma values years of experiences more. In big tech, there are positions value more of experiences than technical skills, but I just feel they are either high level manager position (which is very competitive to get), or product manager types of positions. In pharma, from what I heard recently, promoted to AD are probablty doable, but to director is not so guaranteed. Although being at AD doesn't mean you get paid more compared to the level you can achieve at tech, the hierarchical structure may gives senior people less stress and more power. If I'm in a place where people around me are at the same level but just younger than me, I may feel stressed in tech. 

I know ultimately, choosing a career depends on what a person really likes. But for me, I am never the kind of person who knows clearly what I definitely like and hate. I am ok with coding, programming, but I'm also ok with writing papers and communicating with people outside the domain. I can't find in either tech or pharma, what I definitely love and hate. That's probably because I do not have enough working experiences, but that's also why I really have the difficulty choosing a direction. 

I am closer to the deadline of making a decision. Any comments, options and suggestions are highly appreciated!

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I think not only is the career path more clear/linear for pharma, but the job itself is more clear.  I've never worked in pharma, but because of the regulatory aspects and the goals of clinical trials, I doubt there is much ambiguity.  Being a data scientist requires a certain level of comfort with ambiguity as there is often a lot of freedom in the job and it's not totally clear what you should be doing.  Some people might love this, and it might drive some people crazy.  I was more on the latter side, so I totally understand where you're coming from in your assessment of the pharma advantages.

If you have an offer at a FAANG-type company, I would heavily consider it though.  You're not going to make that type of money in pharma.  Maybe the starting salaries are similar, but once you start acquiring more stock after a few years, I don't think the compensation will be comparable - you can save close to 6 figures a year at these jobs when you're starting out.  Work a couple years at one of the companies with a promotion or two, and you will be set for a manager position somewhere.  After one year at a FAANG company, I was getting final-stage interviews to be heads of data science at  known companies.  I see people on LinkedIn all the time who get jobs at average companies, get a promotion or two over the course of 2-3 years, and then are a manager and can then get manager jobs anywhere.  I think there's a lot of opportunity to quickly move up to an extremely well-paying career.

If you're unsure about which you'd like to do, another advantage of tech is that the options are pretty broad and job-hopping is common.  You can try out a new job every year or so until you find what you like.  I'm not sure if pharma is like this.

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2 hours ago, bayessays said:

I think not only is the career path more clear/linear for pharma, but the job itself is more clear.  I've never worked in pharma, but because of the regulatory aspects and the goals of clinical trials, I doubt there is much ambiguity.  Being a data scientist requires a certain level of comfort with ambiguity as there is often a lot of freedom in the job and it's not totally clear what you should be doing.  Some people might love this, and it might drive some people crazy.  I was more on the latter side, so I totally understand where you're coming from in your assessment of the pharma advantages.

If you have an offer at a FAANG-type company, I would heavily consider it though.  You're not going to make that type of money in pharma.  Maybe the starting salaries are similar, but once you start acquiring more stock after a few years, I don't think the compensation will be comparable - you can save close to 6 figures a year at these jobs when you're starting out.  Work a couple years at one of the companies with a promotion or two, and you will be set for a manager position somewhere.  After one year at a FAANG company, I was getting final-stage interviews to be heads of data science at  known companies.  I see people on LinkedIn all the time who get jobs at average companies, get a promotion or two over the course of 2-3 years, and then are a manager and can then get manager jobs anywhere.  I think there's a lot of opportunity to quickly move up to an extremely well-paying career.

If you're unsure about which you'd like to do, another advantage of tech is that the options are pretty broad and job-hopping is common.  You can try out a new job every year or so until you find what you like.  I'm not sure if pharma is like this.

I think you are totally right. I do realize the money difference and yes it's an offer from FAANG. That's why I struggled a lot because I know this is the type of opportunity that I really appreciate and might feel regret if letting it go. In pharma, I think it's also easy to find other similar pharma companies but the difference is that you still do the similar work. The reason to leave a pharma for another pharma is because you want to get promoted faster. If you hate the current biostatistician work, you have to leave pharma. So you are right, tech has more flexibility in trying different work. Another fact about me is that my significant other is currently working as a DS in tech. So I was thinking maybe it's better if two persons in the family are in different industries to share any possible unknown risk. I also considered the possibility of switching if I regret. It seems that tech to pharma is technically easier if you can accept getting down paid, but pharma to tech practically happens more also because of the salary increase. But either way, I will have to start as a new grad. Tech and pharma seem to be totally different paths and they do not value you more just because you have experiences in the other industry, if I understand correctly. 

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17 minutes ago, lxzqw136 said:

Another fact about me is that my significant other is currently working as a DS in tech. So I was thinking maybe it's better if two persons in the family are in different industries to share any possible unknown risk.

I don't think there's too much risk in the next decade of you not being able to find a job in DS.  Also, if you and your wife are both data scientists at big tech companies, you could be millionaires in 5 years and have the freedom to do whatever you want, really.  The one thing I would not underestimate is the prestige and opportunities that having a job at one of these companies will open for you.  I have friends with PhDs in biostats who struggled to find data scientist opportunities, and I could barely get interviews before the FAANG job, but once you get in, getting any other interview is absurdly easy.  Also, jobs that involve data are pretty common in a variety of industries, so it would be pretty easy to pivot to a different type of role in a totally different industry.

Switching to pharma after a decade might be harder, but I don't think you'd have problems if you decide you didn't like tech in 2-3 years and move to pharma -- this isn't much different than people who get an academic postdoc and then decide to get pharma jobs, which I've seen.

If you're undecided, I personally feel like the potential benefits of accepting the tech job outweigh the risks, which is just being a couple years behind in the pharma world. Heck, pharma companies like Eli Lilly hire data scientists too so you could always switch into something like that.

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Posted (edited)

I am in pharma and I am leaving soon because of the things you listed, because they weren't good fits. I did manage to recently find a more Bioinformatics DS drug discovery type focused position, and they are willing to train me on the domain knowledge thankfully, also because probably my undergrad background is also in a biomedical field so I wasn't a pure biostat/stat person. 

The TLDR is if you really really want to do stats, data analysis, and modeling focused work, then Biostats in pharma will be disappointing. You don't have to go to big tech though, you can look for something like this too in other titled position. 

I had gotten feedback on a similar topic weeks ago and I meant to update that but after a whole month of interviews, I managed to find this as an MS. The interviews for diff DS positions were a mix of presenting data analyses I had already done (I used grad school stuff for these), take-home data analysis, data wrangling tests on coderpad, and leetcode type qs. I bombed the leetcode ones but I passed the ones that had the other 3. And then made the decision, hopefully the right one, on one which seemed more analysis focused on biochemical data. One of the others was academia (which had lower pay) and then another I got the vibe during subsequent interviews it was more DE focused despite claiming to do causal inference and ML. 

Ironically, Biostatistics is a good fit for people who want occasional simple statistics and more focus on writing, communication, FDA/regulatory stuff. If you want to use more statistical methods and have it focused mostly on programming, modeling, etc though then DS, ML engineer are better. 

For me, what drew me to biostatistics major was the data analysis and modeling, so it turned out that being in pharma in a Biostat *title* was an extremely poor fit for this. I've hated writing ever since middle school and the documentation was painful and stressful for me more than learning advanced programming, ML/DL, and data analysis. I think often times there is a common misconception in school that STEM is "harder" than humanities, social science, writing etc and there are definitely people for whom its the opposite and this side is fortunately or unfortunately depending on the person a major part of biostatistics titled jobs. You have to want to get better at it and improve over time to be successful in Biostatistics, and it's something I had near 0 interest in my entire life. It is true you won't be competing with younger people who could be sharper technically when you get older though

I think most likely you will have to pick 1 and then see how you like it in the 1st year. This is how I have ruled out all Biostatistics jobs in the future for me. 

 

Edited by untzkatz
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