Walk Alone With The Perfect Data Science Resume

Nov 30, 2018 | 30138 Views

Breaking into the world of Data Science can be tricky, but writing a killer resume gives you a better chance of landing a job in this highly competitive field.

There are a few simple steps you can take to build a resume that gets noticed by recruiters:

Understand Applicant Tracking Systems.
If you haven't heard of an Applicant Tracking System (ATS), it's software used by companies that receive lots of job applications, and it chooses which applications to forward to the hiring manager and which applications are automatically responded to with a rejection letter.

There has been a movement lately to create these gorgeously designed resumes. You'll see people Tableau-ize their resume (ie. creating a resume using Tableau), include logos, or include charts that are subjective graphs of their level of knowledge in certain skill sets. No matter how pretty your resume looks, an ATS probably isn't going to know how to read graphs, logos, pictures, or tables. Don't use them.

To test if your resume is going to be parsed well by ATS, try copying the document and pasting it in WORD. Is it readable? Or is there a bunch of other stuff? You can also try saving it as plain text to see what it looks like. As data-loving story tellers, I understand the desire to want to show that you're able to use visualizations to create an aesthetically appealing resume. However, it is more important that your resume gets into the hands of a human.  At the end of the day, content is king, keep the aesthetic classic (and parsable by ATS).

Choose your words carefully:
The skills and experiences you choose to feature on your resume need to both convey that you have the technical chops required of a data scientist and demonstrate your results driven, problem solving and self-managing competencies.

While it is easy to list job duties, it's another thing to reword them effectively to highlight your true strengths and demonstrate your ability to improve the business. Your bullet points should be full of action verbs and results, even if you need to stretch yourself mentally to identify these.
For example, have you automated a process that saved hours of time manually doing a task?  That time saved is business value.

Reading job descriptions to see what companies are looking for will help you find consistent themes.  If you look closely, you'll see there are a lot of skills listed that aren't necessarily technical.  Make sure you shine when speaking to these softer skills.  But of course, these softer skills need to be demonstrated in a way that still demonstrates an action and result.  Do not just put a soft skills section on your resume and list a bunch of words without context.

And, always try to include verbiage from the actual job description for the job you're applying to. You might want to sound fancy, but empirical bayesian 3-stage hierarchical model probably isn't on the job description. The person in human resources who doesn't have a data science background is not going to know whether that is relevant or not.  Again, looking at multiple job descriptions and trying to gauge what type of language to use on your resume is helpful.

Gain experience at your current job:
If you currently have a job, do you have access to SQL? Does your company have a data warehouse or database? Can you file a ticket with the service desk to get SQL? Can you then play with data to make your own project?

You could even go a step further and bring data from the database into R or Python. You could make a nice decision tree that answers a business questions then wonderfully and concisely place your results of the project on your resume.

If you really want to make your resume stand out, show that you can automate an every-day task that's repeatable. This demonstrates your ability to increase efficiency, thus saving a business time and money. If you've done data science projects on your own to round out your resume, add them to your resume, and make sure the bullets are full of action verbs and results.

R and Python are open source, you can use open source SQL such as DBeaver, and Google just came out with a dataset search. The world can really be your oyster, but you'll need to market these go-getter skills effectively.

Source: HOB