What are Data Science recruiters looking for in a resume?

Rashi Desai
Jun 15, 2020

Insights from conversations with DS recruiters.

Preparing for a good Data Science resume for an interview or a networking event never gets easy, does it? There is a significant amount of learning, exploration, understanding, and analysis that goes into preparing for that one “hired” resume.

Data Science being an interdisciplinary field, there are a lot of things that a data scientist should know; algorithms, scientific methods, processes and systems to extract insights from data and make decisions based on that knowledge.

No matter the amount of work you’ve put into your Data Science journey or what data science certifications or courses you have done in the past, an interviewer will surely throw you off with a set of questions that you didn’t expect to be coming your way or didn’t have the skill set listed on your resume. But that’s what you got to face, right?

In this story, I have listed points that a recruiter expects to see on your Data Science resume curated after talking and interviewing with over 80+ Data Science recruiters and employers.

1. Curating a resume per the job description

Employers always look for the “relevancy” of your resume to their job description. Even with a robot glancing through your resume, they care more about you wanting a career in data science than they do about you wanting a career with them. Before you start to assemble your data science resume together, make sure you know who you’re sending the resume to; the business expertise.

If the job description is for say, a financial firm or food & beverages, you can always attempt to show at least some domain knowledge. Realistically, your resume won’t be wildly different for each application you make, but it should be somewhat different.

A recruiter from Discover I met told me “We appreciate resumes that distinguish itself from the pool; we like candidates who want THIS job rather than just ANY job.”

Applicants sometimes fail to understand the importance of changing resumés per the job description and this usually encapsulates one of the major reasons for rejects: domain expertise. The recruiters look if you are a good fit for the company, for the department, for the kind of projects they work on? Does your data science resume reflect the fact that you’re a good fit?

2. Picking the right keywords

If it’s data science, it has to have parts or forms of databases, Machine Learning, statistics,

In addition to researching for fit, it’s important to pick the right words that are right for you, as well as one that demonstrates your potential fit to the recruiter. Resumes are mostly scanned by the applicant tracking system (ATS). ATS resume scanning software is designed to scan a resume for work experience, skills, education, and other relevant information.

Keywords for Data Science Hard Skills through ATS

Make sure your resume’s keywords and your experience are formatted in a way that reflects the company’s mission and vision. (We are trying to be a perfect fit!)

The company’s resume scanning software usually are programmed to pick resumes with particular keywords, so even if you want to throw in synonyms of the common Data Science keywords, that should work. But for that, reading the job description is quite important.

Sometimes, including the keywords in the cover letter helps too. Include keywords in the body of your letter making sure that they match the most important keywords and skills mentioned in the job listing.

3. Point out your expertise in necessary skills

Eyes on the price!

A recruiter from Disney mentioned precisely on this: “A recruiter wants to see if the candidate has really done something for the position she is applying to. If we see the skills, projects, courses or certifications in the exact lines with what we are talking right now, you and us, both are good.”

Formatting in resumés plays a quintessential part. When it comes to emphasizing your work, the resume should exactly point out why the recruiter is interested in you.

A data scientists’ resume should demonstrate how good you are at managing data and its, soft skills emerging from the projects.

Another recruiter from Bank of America pointed out to the projects section on my resume and said: “If you have worked on improving the level of aptitude for a given skill, domain or technology, it will definitely be evident from your resume. That’s what we are looking for; a constant cycle of learning and implementing those.”

4. Your X factor

People, in general, would always consider me to be foolish to include an awards and hobbies section on my resume rather than a full-fledged opening statement: the summary.

I chose not to shy out from the unique interests and achievements I had.

With a probability of 7/10 that resumes looking very similar to each other, including a section or content that identifies YOU from the pool always helps. Hobbies on my resume included stock trading, blogging, and Indian Classical Dance and for most of my interactions with recruiters or interviews, I was asked to talk more about it; the goal being a novel conversation.

For my interview with Intuit, the recruiter herself was trained in the same dance form as me and she did have a conversation with me on the same for a considerable 3 minutes, which is substantial in an interview.

While interviewing with Charles Schwab, the recruiter asked what stocks I’ve owned to date and if I am looking for a new stock to invest.
For both the times, I had the perfect answers to all their questions.

Again, such sections are not just to differentiate yourself from other candidates but to introduce a new tangent of conversation with the recruiters; one that shows out-of-class engagements.

5. Write Concise Bullet Points

Not all recruiters are the domain experts unless it is a targeted info session or an actual hiring process.

Long bullet points on what you did on a project definitely hurt!

Recruiters in large career fairs TAKE A LOOK at resumes, quite literally! Having attended career fairs with more than 100 candidates waiting to talk to a recruiter at the same booth, per my observation, if the recruiters spot a project, skills associated, or work experience that would make you “a good fit”, the look transpires to a glance.

For instance, for a Machine Learning project you did, how would you document that for a recruiter’s notice?

  • OKAY, NEXT: Trained and optimized a random forest machine learning model on a dataset to predict the quality of wine with an accuracy of 99%.
  • GOOD: Trained a random forest to predict wine quality with 99% accuracy.

The key is to be concise, to-the-point. Avoid implied or redundant phrases, rearrange verbose phrases or avoid the too-wordy prior experience. Paving way into the depth of

Next, emphasize the numbers or metrics in your resume in bold to exhibit the impact you were able to deliver, instead of merely mentioning wordy superlatives.

  • OKAY, NEXT: Significantly improved conversion rates and drastically reduced bounce rate using an SVM.
  • GOOD: Doubled conversion rates from 0.5% to 1% and reduced the bounce rate by 18% using an SVM.

Including small but substantial takeaways on your resume definitely helps to get more visibility and interaction.

Thank you for reading! I hope you enjoyed the article. Do let me know what your resume already had v/s things you plan to update on your resume. Wishing the best to people looking out to get hired this season!

Happy exploration!

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

Know your author

Rashi is a graduate student at the University of Illinois, Chicago. She loves to visualize data and create insightful stories. She is a User Experience Analyst and Consultant, a Tech Speaker, and a Blogger.

“What are Data Science recruiters looking for in a resume?”
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