I have a PhD in CS from UC Santa Cruz, and I was hired by Google out of school. The key is to do internships: just like undergrads, you have to prove to companies that you can do the work, and internships are the best way of doing that. I did two internships at Google, and did the conversion interviews.

What I don't understand is why you are applying to Google if you're doing a Physics PhD. Why become a doctor of Physics and then throw it away trying to apply for jobs that have zero overlap? You're not trained for the job, and I strongly suspect this is the problem the OP was having. Why aren't you applying to NASA or SpaceX or something?

I hear it's actually not that uncommon for physicists to become data scientists. More importantly though, I was successful in physics as an undergraduate, and it's sort of the academic culture to continue onto to grad school (as well as the culture in my family). In any case, I could always leave grad school if I want to, but I think it's also helping me gain a better computer science background (I've only taken 3-4 computer science classes so far...)

I'm not applying to NASA or SpaceX because I'm really interested in consumer tech, and always have been. Honestly, maybe I have the total wrong idea for what I want to do, and I understand that. That's again why I want to do an internship.

I actually have a Google interview in a couple of weeks. If you don't mind me asking, did you work at Google as a researcher or software engineer?

There are very few people who actually work under Google Research, everyone else is a software engineer (including everyone who worked on my research paper with me [1]). The trick is to realize that there are many product teams for which what you'll be doing are researchy style jobs. These are usually things where you're working on the backend (Compute Platform, Search, Google X) rather than the frontend (Javascript UI stuff). While the job description is software engineer, for those reasearchy jobs, you're doing work that's very similar to what you might be trying at the university lab. Except with a lot more data, and a lot more computing power behind you.

Matt Welsh has written a number of times about his job change from Harvard Prof to Google, and he's a software engineer by title AFAIK, but he's clearly doing research work.

[1] http://research.google.com/pubs/pub41145.html

I like the internship idea. However, I should add that these should be non-research. I did internships at industry labs - this seemed like a good idea at the time since I was prepping for a career in research. Now that I want to get out of it, those research internships aren't worth as much as the Google internships.

As a someone who is 5 years out of his PhD (working at a research lab), I'm a bit perplexed at how I can make up for the lost years of industry experience. The best idea I have now is to start my own company.

I just finished my PhD in theoretical physics and had a relatively easy time finding a "data scientist" like job in the tech industry. I had two jobs offers from two interviews, neither of which were very close to my PhD research.

I did do an industry internship toward the end of my PhD and I think it made a big difference. Not only did I have the experience to show potential employers that I was serious about going into industry, but I also learned more about software engineering in 3 months of my internship than 5 years of academic coding during my PhD. And I ended up with an offer from the company where I did the internship.

I actually think it's an even better idea to do a software engineering internship early in your PhD if you're doing some sort of computational science, because you'll pick up skills that nobody will teach you in grad school but that are also valuable for writing academic code.

As you know, industry internships are way outside the norm for physics, but they're pretty standard for computer science grad students. In my view, not doing an internship was this guy's first mistake, although I have no idea what to make of the fact that he had no offers after 30+ interviews, which does strike me as highly anomalous.

Just so I understand, you actually did a software engineering internship, but ended up in a more data science role, right? I'm curious just so I better understand if even the standard software engineering role internship helps for more than just software engineering jobs after grad school.

It also strikes me as odd that this person had no offers after that many interviews, but the truth is that there are a lot of other factors that go into these decisions. Maybe this person wasn't the best at communicating? Maybe this person sold himself as an all-around generalist, and the companies didn't really know what to do with him?

Otherwise though, I'm happy to hear about physicists going into tech!

No, my internship was also in a data scientist type role. I was thinking of data science as a sub-specialty of software engineering. But I do expect that any internship, even if not in the exact role you are looking for, would help your employability.

Why are you doing a PhD in physics if you don't want to do research in physics? A PhD in physics won't "stifle" your ability to get jobs you want. The thing that will "stifle" your ability to get jobs you want is not having the relevant skills or not being able to play the game of interviewing.

E.g. I interviewed a physicist for a data science position. While he had worked on a lot of problems which could be categorized as data science problems, 95% of the problems he was working had one specific tool that he used, Principal Component Analysis. It was kind of hard to get him to think of another ways of solving a problem.

Part of the reason I'm doing a PhD in physics is, of course, because I enjoy the work. Everyday I come in and I always feel like I have something to learn from the people I work with. I feel like I grow as a person because I'm constantly learning new things from my peers - whether that be something purely theoretical, hands-on electronics work, cleanroom work, machine shop work, optics work, or, yes, coding. Could the same be true in industry? Absolutely. Is it nearly as guaranteed? I'm sure not. I definitely have friends that are starting to gain the golden handcuffs at their companies, and they've only been working for 2 years.

Moreover, I don't think it's fair to say that people waste their time doing a PhD in physics. Sure, they could be learning a lot more job-specific skills, but the point is that the people who do a PhD in physics absolutely have the ability to learn those things. I'm sure if the physicist used PCA it wouldn't take him long to learn plenty of other ML techniques. I think the skills you learn during a PhD aren't as simple as just listing out the technical things you became familiar with. (Still, I'm certainly considering leaving grad school, but I don't think it's an obvious decision.)

Of course, I think there is a lot more to a job than the technical skills you bring to the table --- your ability to communicate, your understanding of the market & product, and your ability to work well with others. (Yes, I'm fully aware physics people don't often hold gold medals in these categories). I think that doing an internship could help in these respects, and it's why I'm interested in doing one.

> Moreover, I don't think it's fair to say that people waste their time doing a PhD in physics. Sure, they could be learning a lot more job-specific skills, but the point is that the people who do a PhD in physics absolutely have the ability to learn those things. I'm sure if the physicist used PCA it wouldn't take him long to learn plenty of other ML techniques.

I sympathize with your situation as someone who accidentally got into computer science. As much as I have tried to portray myself as a fast learner or someone who can learn on the job, a secret in Industry that I have learnt is that very few people care. Most managers are looking for someone who can fit a resource need yesterday. To illustrate: the other day I was talking to a recruiter from BigCo, the recruiter strongly suggested that I write code on the whiteboard in an actual programming language rather than pseudo-code. This seemed strange because BigCo is known for their algorithm intensive interviews. Then, I was talking to a friend who works for BigCo2 and it all made sense: if there are two candidates A and B, of whom you can hire only one and both A and B solve the algorithm but B appears to be able to write the code with very few mistakes on the board, you are probably going to hire B mainly because B appears to fit the hole in your organization. Now, as much as people like to protest they are not like that, companies are hierarchies of managers, managers who have concerns and reports to file. The physicist who used PCA would probably have been able to learn everything else in 6 months. Who is going to spend six months paying him a shit ton of money while he learns or maybe doesn't? I am not saying everyone who gets hired is hired with an expectation of being productive from day 1 but you have to be productive sooner than later.

Ultimately what am I saying? If you are thinking of going into industry; get real. Get all the skills necessary, your resume in order. There will be millions of blog posts bemoaning the disconnect between industry and PhD or how the interview process is broken. However, all that fluffy bullshit is not going to bring a paycheck down the line. What is going to get you interviews is having "good internships", code on Github. What is going to get you a job is the ability to convince a collection of random people that you are worthy of getting a job. Sure, the stochastic nature of the game means there will be some moron who thinks that him dropping out of high school and spending the last five years writing spaghetti code for industry makes him superior to you. Or you may have an attitude that you spending five years writing research papers makes you better. Ultimately, all that doesn't matter.

Some of us think that contributing to human knowledge is worth five years of our life, even if we might not be able to continue contributing.

When I was in grad. school, I thought the idea of doing a PhD was to train for a lifetime of contributing to human knowledge. Not a lot of PhD theses end up as a contribution to human knowledge. Sure, there are the 1% that do, those people don't typically have the problem of not being "able to continue contributing."

Agreed. You get a PhD for you, not for science. I would hazard 99% of PhD dissertations get read cover to cover less than 5 times. And I'm including the committee and author on that number.

That is, broadly, the ideal. Your PhD thesis is your first masterwork that qualifies you to call yourself a real researcher, and you go on to do many more along the length of your career.

In actual fact these days, there simply isn't a career model set up to support that anymore. Your PhD thesis is now supposed to qualify you for your first postdoc, your first postdoc or two qualify you for a permanent position, and you earn tenure or permanent contract in your permanent position, and then you can just focus on contributing to knowledge rather than on careerism.

I am currently working on my PhD Biomedical Engineering (3 years in), and I have mixed feelings on this. Certainly most PhD dissertations are a joke, but during the first few years of a PhD you are acting as the arms and legs of your professor, who, if they are any good, is capable of pushing forward human knowledge using you as an "instrument".