I'm often asked what it is about data science that I find so interesting. It's a great question - the field has absolutely exploded over the last few years, and I'm sure we all have our own reasons for entering it. For me, I love that data science lets me tell stories through numbers, while acting as the keeper of the truth for my company. 

I've always loved math, dating back to preschool, and AP Statistics was one of my favorite classes growing up. But there was also a period where I wanted to be a writer. I enjoy the challenge of taking an idea and conveying it to someone else through words. Data science, to me, is the bridge between math and writing. You take an idea, support it with numbers, and express it to another person. You make hypotheses and chase down the evidence like an investigative reporter. It's fun. 

I also often remember what a professor told our class on my last day of grad school. I'm paraphrasing, but he said something like, "People will trust you to read the data and tell them what's happening. Nobody else will have the same knowledge or expertise, so take that responsibility seriously and be committed to the truth." 

I remember being on a call with investors during my stint at a startup earlier in my career. We had submitted a bunch of documents for our Series C fundraising round a few weeks before, and one VC fund had a few followup questions. As the only data scientist at the company, I was in the meeting, laptop open, ready to run queries in case they wanted to see the data in a different way. I flashed back to what that professor has said, and I knew that I was the only person who knew the data well enough to handle any new questions. It was fun. 

So that's why I love data science - what about you? What got you interested? 

A few years ago, when I was a data scientist at Microsoft, I spent some time at one of our London offices. There, I worked with a recruiter to find someone to replace me after my stint ended. The team had never hired a data scientist before, so I helped with reading resumes and conducting phone screens.

It's crucial to understand that recruiters usually have to read dozens of resumes in a short amount of time. (Side note: be kind and patient when waiting for a response!) I eventually learned to look directly at a couple key points - where the applicant went to school and what they studied, and their current job and job title, got priority. We'd look for evidence of passion and experience working hands-on with data, and imagine how the applicant's skills would match up with our requirements. 

It was a very difficult process that taught me a lot about getting noticed by a recruiter. In this post, I’ll share some of what I’ve learned about crafting a data science resume that will make you stand out.
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(Note: This is aimed at people trying to get their first data science job – I’ll use my own resume as an example.)

First, make sure that you’ve got the basics down – stick to a standard template with your contact information at the top, followed by sections for your work experience, education, interests, etc. (I just googled “resume templates” and found a bunch of good ones.) Write out detailed bullet points under your work experience, each one describing a different skill. Have a friend make sure there are no misspellings or formatting errors.

This is now your base resume. When I first started applying for jobs, I would reuse the same exact resume in every application. I eventually learned to tweak the base resume to more closely match the job description. For example, the Microsoft job listed SQL as a desired skill, so I shuffled my bullet points around to prioritize this: 

Knowing what I do now, it's easy to see why this helped me - the recruiter's eyes moved to my current job, saw that I was an analyst, and only had to read one more line to see two magic words: "SQL" and "data". At this point, I probably had them intrigued enough to keep going. 

In the next few bullet points, I was careful to emphasize a few different skills - Excel, R, presenting, working with upper management. One by one, I was knocking off different sections of the job description. Because each line said something different about me, the recruiter likely felt like it was worth it to keep going. Remember, he or she will be really busy, and you can't afford to lose their interest.

Next, put whatever sets you apart the most at the very top, though it should be either work or education. (If you're the best ballet dancer in the country, I'm proud of you, but save that stuff for the extracurriculars and interests sections.)  I thought my master's in statistics did this more than my job in economic consulting, so I led with that: 

But note that I didn't just mention my education and move on. I listed the courses that I thought were most relevant, in order. (E.g., if the job description had been more programming-heavy, I would have put Statistical Computing Software first.) I followed up with some actual projects to really drive home the point that I had worked with data before, even if my current title didn't have the word "data" in it. 

So those are the basics! To recap:

1. Get your base resume ready with detailed information about your work and education. Make sure your contact information is up to date, and that there are no typos. 
2. Shuffle bullet points around to emphasize the skills that are most relevant to the job you're applying. 
3. Lead with either your education or your work history - whatever sets you apart the most.

You might be thinking...

• I didn't study statistics, I majored in English! (Or History, Viticulture, etc - something less quantitative.) 

This is usually OK if you took enough data science-esque classes (economics, math, programming, etc.). If that's the case, list them individually, and maybe even include your GPA in those particular courses. 

• I didn't take enough of those classes in college. 

I didn't either, which is why I felt it necessary to go back and get my Master's. But I understand that's not feasible for everyone. Consider taking classes online, through something like Coursera. One of my best hires ever had no previous data science experience, but his resume still caught my eye because of how many data classes he had taken online. Remember, passion for the subject goes a long way. 

• ​My current job doesn't involve data in any way. 

Are you sure? Do you give presentations that might be enhanced with an Excel chart? Could you get involved with A/B testing new marketing campaigns? Write a query to automate something currently done manually? 

• Nope, none of that. 

Well, keep taking those online classes. You can also come up with your own fun analyses and write them up, like I did a million years ago here. 

Try out some of these tips for your next application. And as always, feel free to get in touch with further questions!

Welcome to my blog. Here I'll write about life as a data scientist, as well as interesting side projects and tips for beginners. Stay tuned!