Getting a Data Science Job is not a Numbers Game!

April 29, 2019
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[This article was first published on Learn R Programming & Build a Data Science Career | Michael Toth, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Getting a data science job by throwing darts at a board

My First (Non Data Science) Job Search

Let me tell you a story about my first job search. It was 2010, and data science jobs weren’t really a thing yet. I’ll get to that in a minute, but bear with me first because there’s a point to all this.

At the time, I was a junior at the University of Pennsylvania, where I was studying finance and statistics.

Every year, there was months-long on-campus recruiting season where all of the students frantically applied to secure prestigious jobs and internships from big banks and other financial companies.

And I knew I NEEDED to get one of those jobs.

But unlike a lot of the other students at Penn, I didn’t grow up in New York City. I was from a working class family in the midwest. So when I started my job search, I had no connections in the industry. Zero.

Obviously, my applications weren’t going to be fast tracked. But more importantly, I had nobody to ask questions about the different job opportunities available.

It felt like everybody else in school understood all of the different types of jobs in finance and which ones to apply to.

Not me. I had no clue. But, what the hell, I figured. I was smart. I was near the top of my class! I had a 3.8 GPA and an extremely difficult and technical course load. Somebody would hire me! And I didn’t really care what type of job I got, I just needed a job. So I applied to everything.

I applied to trading jobs. I applied to investment risk jobs. I applied to investment banking jobs. Over 100 jobs in total. And then… crickets. Most of the companies didn’t even respond to me! It was demoralizing.

I was freaking out. I needed to land a job. My first job was extremely important, and it would set me up for my entire career. I worried if I didn’t get a job now, I would have to move back to Ohio after I graduated, probably closing the door on a prestigious finance job forever.

My Big Break

Finally, after weeks of anguish, I got a big break. I had been asked to interview for one of the jobs I had applied to, a technical trading job at Allstate.

I was so excited. When I went to the interview, I met a guy named Mark. Mark was the one who had decided to interview me, and the hiring decision was ultimately his to make.

Mark and I got along well, from what I remember. He had a relatively senior role at the company, but his schooling background had been similar to mine. He’d studied finance and engineering, and he was looking for somebody smart with a strong combination of finance and technical skills.

He must have seen some potential in me, because shortly after the interview he offered me the job. Of the hundred-or-so jobs I applied to, this was the only offer I received.

What I Learned

So what’s my point? I tell you your data science job search is not a numbers game, but then I tell you how I applied to over a hundred jobs to get a single offer. What gives?

Here’s the thing: I got this job because I had the exact combination of finance and technical skills that Mark was looking for. Most of the other jobs I applied for, I never had a shot of getting, because I didn’t have the right background. All of those applications, and all of my effort in applying, were a waste.

If I had instead focused on only applying to the technical finance jobs where I had a unique advantage, I’d have had a much higher success rate.

Getting My First Data Science Job with a Strategic R Blog Post

My experience during that application process changed how I thought about job searches. Years later, when I was trying to break into data science from my finance job at BlackRock, I took a different approach.

I knew I wanted to get into data science, but with no formal training and potentially hundreds of applicants for each position, I also knew I needed to stand out. So I scoured job boards searching for jobs where I knew my skills would give me an advantage.

I was very good at financial data manipulation, something the majority of data scientists know absolutely nothing about.

So when I found a financial technology startup specialized in online lending analytics that was looking for a data scientist, I knew it was the perfect opportunity for me.

What did I do? Did I send in my application and then just hope they contacted me?

Nope.

I wrote a detailed blog post analyzing historical default rates for Lending Club loans. This was exactly the type of work I’d be expected to do at the company. I probably spent 10 hours doing the research and analysis and then writing that blog post.

What do you imagine happened? They called me in for an on-site interview. And I pretty much breezed through it. The interview was primarily to assess my cultural fit, not my data science skills, because I’d already proven to them I was capable!

Avoid the Spray and Pray Approach when Applying for Data Science Jobs

When you carefully select the companies you’re going to apply to based on an alignment of their needs and your skills, you can dedicate more time to each application. That extra time is how you stand out in a pool of hundreds of job applicants for a single position. You could:

  • Write a blog post showing your ability to do the work
  • Send them a detailed list of metrics they should be tracking to improve their business
  • Analyze a relevant public dataset and tell the company how they could incorporate that data into their product

The specific thing you do will vary across companies and industries. But if you can do something to add value and differentiate yourself from the hundreds of other applications, you will vastly improve your chances of getting a job.

ALWAYS REMEMBER THIS: The job you’re applying for exists because the company has a problem that they’re trying to solve. They’re not looking for a generic person with a generic set of data analysis skills. They’re looking for a specific person that can help them solve their problems.

That doesn’t mean you need to know everything, but it does mean that you should lean into your specific strengths when deciding where to apply. If you can show the company that you can solve their problems, you become a top-5 candidate immediately.

For your next data science job search, don’t apply for a hundred jobs. Find 10 jobs where you bring unique skills to the table, and try to do something that demonstrates your unique skills to those employers. I promise you’ll find far more success with this approach.


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To leave a comment for the author, please follow the link and comment on their blog: Learn R Programming & Build a Data Science Career | Michael Toth.

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