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What PhDs do wrong (and right!) when applying for Data Science jobs

XKCD.com

If you’re fortunate enough to have a quantitative PhD, you may have thought about applying for an opening in data science.  After all, it’s an exciting and well paid field and a lot of fun too.  Drew Harry recently wrote on this topic.  As a PhD himself he should know.  Here are a few of the observations from his article.

Nearly half of people working as data scientists today have PhDs, though they make up less than 2% of people in the US over 25, so your academic training will command some attention:

Unfortunately, there’s a gap between the rhetoric pushing PhDs to industry jobs and what is necessary to actually get those jobs. I’m going to try to close that gap here by describing what great PhD applicants do, as well as some of the common mistakes we’ve seen.

Make Your Case

The one thing that is non-negotiable for newly minted PhD students is write a cover letter!

With a short employment history that most definitely doesn’t include the title “data scientist” on it, the cover letter is your one chance to sell someone on why you could do a job you haven’t done. Be concise, but address the major questions someone might have about you:

Data science is an interdisciplinary field that requires frequent translation between engineers, product managers, marketers, and executives. The cover letter is your best opportunity to demonstrate your sophistication as a communicator.

Learn the Domain

If you make it to the phone screen, don’t presume you can talk your way through an interview in a domain in which you have little direct experience. You have to close the gap between the domains you’ve worked in historically and the domain you’re trying to move into by doing some serious background research.

The other major piece of the puzzle is understanding the company. You should spend at least half an hour playing with the product or reading about the company to understand how it works. What are the nouns and what are the verbs in the system? How do they fit together? How does the company make money? Who does it sell to? How does it grow? What makes it compelling to the people who love it? Not all companies will ask you detailed questions about their own product in a phone screen, but being specific about why you’re excited about the company or team will be valuable in nearly all situations.

Understand Your Value

Your most recent job was getting a PhD, which can be a bit of a mysterious process to many people. It’s important to reflect on what you actually learned in that process and how to explain it to people. There are two big pieces to this: selling your work and identifying your skills.

The Bottom Line

Just because there’s clear consensus that people with quantitative PhDs can be great data scientists doesn’t mean getting those jobs is easy! Having a PhD is a reason to think you might enjoy that line of work and become successful at it. Don’t lose sight of the fact that having a PhD also makes you a high risk / high reward kind of candidate and take the steps you need to make it a smooth transition.

Read Drew’s original article here.

 

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Comment by nlakidzi edson on March 23, 2016 at 5:21am
Am a software engineer but not yet employeed. Am on intenship programme. What are things that I have to consider when applying for a job for the first time??
Comment by Vinay Mehendiratta, PhD on November 9, 2015 at 10:00pm

right on target.

Comment by Sione Palu on October 27, 2015 at 9:44am

I give high weights about the PhD candidate's list of peer reviewed publications in their CV or their thesis topic.  That in itself showed their abilities. Domain knowledge is something learnable, so it weighs less. We got 2 PhD candidates at the beginning of the year & one of them a computer scientist specializing in machine-learning/computational-linguistic had a list of papers that she (& coauthors) published in various journals (ACM & IEEE). This person just hit the ground running. Another one with a PhD is mathematics & his thesis topic was in market-auctions which involve heavy differential calculus. He too, familiarize himself with the analytics we do quickly. This is because of their ability to learn & grasp difficult topics but not because of their industry experiece, since they were just fresh out of university.

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