Tech job Q&A

I didn't feel like I had my "foot in the door" when I applied, and got, data science jobs. For instance, I hadn't done any data science projects and had only 1-2 related courses on my transcript. (To be fair, I was only applying for an internship and later converted to full-time, so perhaps that's your answer?)

The old joke is that a data scientist is a better statistician than a programmer and a better programmer than a statistician. That's what you need -- statistics and programming. You don't have to be world class in either, though it helps. You just need both.

I did not have a degree directly related to data science. I had studied political science and psychology, when most data scientists study statistics or computer science. (But both poli sci and psych do involve statistics.)

I had always been a hobbyist programmer, but I took time to learn R, which is a very common language for data scientists. (Python is also popular.) I did this through Coursera.

I had also learned Ruby, and I got my start as a software engineer intern after graduating college. I then transferred to the data science department.

Another big benefit for me was that the head of the data science department is a friend of mine, who also helped me get the internship. Skills matter, but so do internal referrals. ;)

It may have become more difficult. At my company, I believe we interview a lot more people for data science than we do Engineering. We seem to have a lot more difficulty finding engineers. That said, this could in part be because our data science seems more interesting than our engineering.

Lots of different skills for lots of different careers: In general, as you advance your career, management and sales skills are fairly useful and transferable. Being experienced and expert in any domain is useful. If you want to do EA research, then academic skills are handy. In tech and research, programming looks useful. If you care about tech and know some maths, then machine learning looks like a good and growing area. That's just off the top of my head.

Areas I personally think are interesting include programming languages, databases, machine learning, cryptography/security, and networks, off the top of my head. Though I suspect that these don't make up a very large fraction of job postings! Lots of jobs are just writing miscellaneous tools to automate various parts of other businesses.

I think it's highly unlikely that macro-level job prospects for generic software development will continue to look as good as they do right now:

• Venture investment in software is large and growing extremely quickly right now (more than 50% yearly last year). Annual venture capital investment is now equal to about 5% of technology industry revenue (~$50b on ~$1T) and probably has an outsized effect on jobs, so a slowdown could put downward pressure on salaries.

• Right now, bootcamps are small (~10% of newly-educated entrants into the tech industry). I'm not sure on what timescale 80k is worried about a "short-term oversupply"--bootcamp graduates won't be a large fraction of tech industry workers for at least 10 years, since college grads are growing as well.

  I'd be more worried about a long-term equilibration of supply: right now there appears to be a substantial amount of money lying on the ground (as bootcamps demonstrate), which suggests the market is not in equilibrium and we should expect equilibrium wages of tech workers to be lower.

When first starting out: learning while collaborating with people.

When going from beginner to intermediate: learning while working on a problem independently.

When going from intermediate to expert: learning from reading previous work/existing codebases

I learned a ton of useful statistics and machine learning by reading textbooks. So far that's been my best source.

1. It strikes me as implausible that the best way to make money in technology is to try to make the most money right away. So I would go a step further back than you and look for good learning opportunities rather than ones which will make a lot of money.
2. To this end, I think a company's degree of sponsorship is often underrated when people are making decisions. I wrote more about this here.
3. Startups are an extremely mixed bag when it comes to learning opportunities. You will be given a lot more responsibility than you would in a big company, but you also receive less mentoring and you will have to do a lot more grunt work that would be outsourced to lower skilled people in a big company. My guess is that the average big company is a better employer than the average startup for people who are earning to give.
4. To the extent you do want to join a profitable startup, I would guess that it's very hard to outperform professional investors. So if they've recently raised funding it's probably best to just assume that valuation is correct, but it could be tricky if they haven't raised recently. If they haven't raised recently because they've got a good cash flow, you could look at EBITDA or revenue multiples; if they haven't raised recently because they can't find investors then that's probably a red flag.

Employee #100 seems a bit implausible. If you joined Dropbox as employee #100 it would be in early 2012, at which point they had just gotten a $4B valuation. It's only gone up 2.5x since then--a mere 35% per year--so you probably wouldn't have done better than a founder over the equivalent timespan. Especially once you take into account the many worse options that were in Dropbox's reference class in 2012, like Fab.

That said, I agree that trying to forecast startups is probably a useful exercise--and maybe even possible to do historically, if you're interested in ones as high-profile as Dropbox. It's an open question to me how efficient the market is here (i.e., are companies with semi-obvious predictors of success likely to offer less equity).

I don't know what the bare minimum to get hired anywhere is, but I know that most medium-sized and up places that you might want to work will hire an entry level employee who looks smart but has a very small amount of actual experience.

A good applicant can write a simple program on a white board and has a project on github, or a past internship, or a dynamic website that they run, to point at. If you think you're on the edge now, these accomplishments shouldn't be too far away.

BSing interviews and lying on your resume? Or even less if you can rely on nepotism? :P

Seriously, there are places that are basically just looking for warm bodies, but I doubt that you'd actually be interested in working there. Perhaps you could be a bit more concrete?

This question is abstract enough that I'm having a hard time coming up with a meaningful answer. Perhaps someone with more experience hiring for webdev roles could fill in? (I only interviewed for webdev jobs, so I don't really know what their minimum was!)

If you can complete this, you can probably get an internship somewhere, and from there you can easily transition to a full-time job.