Data scientists are in demand like never before, but nonetheless, getting a job as a data scientist requires a resume that shows off your skills. At The Data Incubator, we’ve received tens of thousands of resumes from applicants for our free Data Science Fellowship. While we work hard to read between the lines to find great candidates who happen to have lackluster CVs, many recruiters may not be as diligent. Based on our experience, here’s the advice we give to our Fellows about how to craft the perfect resume to get hired as a data scientist.
Be brief: A resume is a summary of your accomplishments. It is not the right place to put your little-league participation award. Remember, you are being judged on something a lot closer to the average of your listed accomplishments than their sum. Giving unnecessary information will only dilute your average. Keep your resume to no more than one page. Remember that a busy HR person will scan your resume for 10 seconds. Adding more content will only distract them from finding key information (as will that second page). That said, don’t play font games; keep text at 11-point font or above.
Avoid weasel words: “Weasel words” are subject words that create an impression but can allow their author to “weasel” out of any specific meaning if challenged. For example “talented coder” contains a weasel word. “Contributed 2,000 lines to Apache Spark” can be verified on GitHub. “Strong statistical background” is a string of weasel words. “Statistics Ph.D. from Princeton and top thesis prize from the American Statistical Association” can be verified. Self-assessments of skills are inherently unreliable and untrustworthy; finding others who can corroborate this (like universities, professional associations) makes your claims a lot more believable.
Use metrics: Mike Bloomberg is famous for saying “If you can’t measure it, you can’t manage it and you can’t fix it.” He’s not the only manager to have adopted this management philosophy, and they are all keen to see potential data scientists be able to quantify their accomplishments. “Achieved superior model performance” is weak (and weasel-word laden). Giving some specific metrics will really help combat that. Consider “Reduced model error by 20% and reduced training time by 50%.” Metrics are a powerful way of avoiding weasel words.
Cite specific technologies in context: Getting hired for a technical job requires demonstrating technical skills. Having a list of technologies or programming languages at the top of your resume is a start, but that doesn’t give context. Instead, consider weaving those technologies into the narratives about your accomplishments. Continuing with our previous example, consider saying something like this: “Reduced model error by 20% and reduced training time by 50% by using a warm-start regularized regression in scikit-learn.” Not only are you specific about your claims, but they are also now much more believable because of the specific techniques you’re citing. Even better, an employer is much more likely to believe you understand in-demand scikit-learn because instead of just appearing on a list of technologies, you’ve spoken about how you have used it.
Talk about the data size: For better or worse, big data has become a “mine is bigger than yours” contest. Employers are anxious to see candidates with experience in large data sets—this is not entirely unwarranted, as handling truly “big data” presents unique new challenges that are not present when handling smaller data. Continuing with the above example, a hiring manager may not have a good understanding of the technical challenges you’re facing when doing the analysis. Consider saying something like this: “Reduced model error by 20% and reduced training time by 50% by using a warm-start regularized regression in scikit-learn streaming over 2TB of data.”
While data science is a hot field, it has attracted a lot of newly rebranded data scientists. If you have real experience, set yourself apart from the crowd by writing a concise resume that quantifies your accomplishments with metrics, and demonstrates that you can use in-demand tools and apply them to large data sets.