Learning git is not enough: becoming a data scientist after a science PhD

If you’re thinking of leaving post-PhD science for data science then doubtless people have told you to learn version control.

They’re absolutely right. You should. But learning git is not enough.

So, in the spirit of A PhD is Not Enough, a great book about careers in science, here’s some advice about moving from academia into data science after completing a PhD in a natural science.

Unlike A PhD is Not Enough, however, this post is not a complete guide to a career. It’s just a collection of (hopefully non-obvious) things that have occurred to me since I made the move myself three years ago.

And to be clear: none of what I say here applies to you if you have a PhD in computer science, mathematics, statistics or the humanities.

Contents

Advice about advice

Data science is a young discipline, and it’s only in the past couple of years that tech firms have recognized the particular value of an advanced natural science education.

This means that the people who’ve being doing data science for long enough to give advice from a position of experience are very different than the average science PhD considering leaving academia.

Conversely, data scientists who recently completed a science PhD simply don’t know what they’re talking about because they haven’t been working (or hiring) for long enough. I include myself in this category.

People are going to seem like they know what they’re talking about, but take their advice with a big pinch of salt.

Why tech companies shouldn’t hire you

Before I get into the practical advice, I think it’s important to know why you and your peers are of interest to a hiring manager in tech.

This is a perfectly reasonable list of things a data scientist should know. On average, science PhDs know this material no better, and often far worse, than anyone who can solve FizzBuzz.

Tech firms are by now all too aware of this. They know that, left alone, a typical science PhD cannot build robust, complex software systems. More fundamentally, science PhDs are often ignorant about the basic tools and conventions of collaborative software development. I certainly was (and compared to an undergraduate CS major, I probably still am).

And yes, most science PhDs are comfortable with some pretty sophisticated ideas from mathematics and statistics. But they rarely have the breadth of a statistics or a machine learning PhD. They often lack knowledge of the particular areas of statistics that come up in industrial data science.

And there’s another problem with science PhDs: depending on their thesis adviser they may have acquired a tendency to treat the word “data” as a plural. The good news is this absurd habit can be unlearned. The other problems are more serious.

Why tech companies should hire you

So why do tech firms hire science PhDs? Science is not perfect, but it’s been pretty successful. And the intellectual posture and methods it uses seems likely to be partly responsible for that success.

It’s difficult to finish a science PhD without acquiring two things:

A deeply ingrained attitude of skepticism toward claims made about data, including your own.

The ability to conduct undirected research programs whose job is to determine whether that attitude is warranted.

Now, It’s possible to get a science PhD without picking these up, and it’s possible to learn them without doing a science PhD. But a random person who has a PhD is more likely to have learned them than someone without.

I’ve met machine learning PhDs, the kind of people who get hired at NIPS and start on $300,000, who neither know nor care about the most fundamental concerns about experimental data such as sample bias and censorship.

I don’t think it’s a coincidence that the recently publicized article about relative merits of men and women in tech was written by a Google engineer who quit grad school before he had to do any research. It was glib, intellectually lazy and arrogant. These are things that a modern graduate research education in a natural science seeks to beat out of you (admittedly not always successfully).

Salaries

OK, that was kind of philosophical and, coming from someone still trying to rationalize the fact that he spent 10 years on a science PhD and subsequent postdocs, probably a little self-serving.

Here’s some practical advice.

Unless you were a very successful academic, you’ve probably never negotiated over money. How do you know if an offer is fair? Data!

There are two useful sources of information. The first is the O’Reilly Data Science Salary Survey (US 2016 edition, European 2017 edition). This survey gives you a formula you can use to estimate what people like you get paid. In my case it’s right to within about 10%.

The second useful source is the H-1B salary database. The salaries of people on H-1B visas are matters of public record. You can search by title and employer. It’s not impossible you’ll find the salaries of people who work where you’ve applied, doing exactly the job you’ve applied for. There’s nothing like knowing the salary of your interviewer to level the playing field during negotiations.

Both data sources are flawed. Naively applied, the O’Reilly formula tells you that you should subtract $5000-6000 from your salary if your gender is female. Correlation does not imply causation though, so you should not lower your expectations or demands based on this (or any other) tendency in their data.

In aggregate, the H-1B data is perhaps even more problematic than the O’Reilly data. It’s dodgy data about a biased sample.

The H-1B sample is flawed because H-1B visa holders are atypical. In some situations they are hired precisely because they have lower salary expectations than US residents (paying them less than the prevailing rate violates the terms of their Labor Condition Application, but it happens). In other situations the employer puts up the with expense and delay of the visa application because they have unique skills that also make them more valuable.

The H-1B data is flawed because the salary information is only recorded at the time the offer was made, since which it has presumably increased, and it does not include bonuses or non-cash compensation.

But some information is better than none.

How finding a job is different than in academia

The visa situation is much tougher

In most countries, universities find it relatively easy to get visas for students, postdocs and faculty. Tech companies generally find it much harder.

In the United States, H-1B “application season” is January through February. You need to have the offer by March so the employer can submit by the first week of April. You then have a roughly one in four chance of being selected in the lottery (or slightly higher if you did your PhD in the United States). If you’re selected, you can’t actually start work until October 1.

If you have very strong academic credentials, can afford to pay the filing fees yourself, and are patient then you may be in the running for an O-1 visa. This has upsides and downsides, but the upside is there is no lottery and no deadline. If you’re going this route, you’ll probably need your own attorney. I cannot recommend McCormick & Dooley highly enough.

Self-promotion is still valuable

Compared to academia, it’s less necessary to give talks, write publicly and network. But it doesn’t stop being incredibly effective. So if it’s something you enjoy, don’t stop.

Changing jobs is easier and more common

A successful academic career consists of a PhD, one or two postdocs, and a tenure track position you remain in for the rest of your working life. Opportunities to correct course come up once per year, and don’t look good on a CV.

In tech, it’s normal to change jobs every couple of years. By all means try to get career decisions right, but they are far, far less fraught and irrevocable than in academia.

Not everything has an application form

I was an astronomer. When I was applying for jobs in academia I would go to the AAS Job Register, a website that lists every single vacancy in international astronomy. It’s a finite set. The employers have reputations going back centuries and I’d been reading my potential managers’ work for years.

When I left academia, that was no longer true. Like me, you’ll have to do huge amounts of research to find the vacancies and learn about the employers, their products and their teams. And you’ll never find all the vacancies, because many of them are never advertised.

I think Cathy O’Neil sums up the danger here well. She’s talking about Harvard grads, but this is also true of science PhDs:

[Harvard grads] are vulnerable to Wall Street investment firms and to things like Teach for America because they have application processes at all. But life, normal adult life, doesn’t have an application process.

When you leave academia you enter “normal adult life”. The lack of an application form is exciting and overwhelming. You’ll get the hang of it.

But until you do, one of the dangers is that boot camps will be seductive because they’ll feel like academia. In some cases they can be exactly what you need to jump-start your career. But they are not always necessary. If you’re planning to do a boot camp, think carefully and honestly about your reasons.

How to choose where to work

If you’ll be working in the engineering division, then make sure you’ll be working alongside experienced software engineers and data engineers.

And where ever you’ll be working, make sure they have data. There are times to take a job where this isn’t the case, but your first job after a science PhD is not one of them.

Think about how data science relates to the mission of the company. To that end, I love sharing this quote from Stitch Fix:

A Data Scientist should look for a company that actually uses data science to set themselves apart from the competition. When this happens, the company becomes supportive to data science instead of the other way around. It’s willing to invest in acquiring the top talent, building the necessary infrastructure, pioneering the latest algorithmic and computational techniques, and building incredible engineering products to manifest the data science. “Good enough” is not a phrase that is uttered in the context of a strategic differentiator.

I don’t agree that a data scientist “should” work for a company where data science is a strategic differentiator. Plenty of people have fulfilling careers and do great work when that isn’t the case.

But the distinction I think they’re getting at — do you want your work to be the center of attention or not? — is a very useful one to think about.

Finally, think about the impact you want to have on the world. One of the reasons I left academia was to have more of an impact. But the corollary of having more impact is having more scope to do harm. Data scientists enable surveillance culture and recapitulate discrimination at scale. For more on this, read Weapons of Math Destruction and Big Data’s Disparate Impact.