Homework during the hiring process…no thanks!

August 17, 2015

(This article was first published on Mathew Analytics » R, and kindly contributed to R-bloggers)

For the past four months, I’ve been on the job market looking for work as an applied statistician or data scientist within the the online marketing industry. One thing I’ve come to expect with almost every company is some sort of homework assignment or challenge where a spreadsheet would be presented along with some guidelines on what type of analysis they would like. Sometimes it’s very open ended and at other times, there are specific tasks and questions which are put forth. Initially, I saw these assignments as something fun where I could showcase my skill set. However, since last month, I’ve come to see them as a nuisance which can’t possible be a good indicator of whether someone is ‘worth hiring’ or not. I get it, companies often get inundated with resumes and they need effective processes to sift through them. And I get the value of getting some document which outlines how an applicant thought about a problem and generated some valuable insights.

With all that said, do we seriously think that homework assignments and challenges during the hiring process are the most effective way of getting the “best candidate” (whatever that means). I don’t have any data to suggest either way, but am inclined to believe that companies and analytics hiring managers need to develop better ways of assessing the quality of candidates. It these assignments are really about assessing who is most serious about a role to spend a few hours of their free time answering some ‘simple’ questions and putting together some basic lines of R or Python code, then so be it. But I think a better process can be put forth that allows companies to find the right candidate.

I’ve been part of the hiring process and I’ve also gone through months of looking for employment. Based on my experiences on both sides of the table, here’s my view of what is most effective when looking for analytics professionals, applied statistician, or data scientists. Ultimately, my feeling is that the only way to assess whether a candidate is worth hiring is by effectively testing prospective candidates in a more formal manner.

Part 1: Quantitative Skills
To assess a candidates quantitative proficiency, here are some techniques that work well based on my previous experience.
a. Put together a document with an existing business problem and some of the analysis that’s been put together to answer them. Ask the applicant for suggestions on the limitations of the current approach and what they’d do if that project was handed to them.
b. Put together a basic statistics test which inquires about simple probability theory and inferential statistical principles. Ask the candidate to answer those questions in an informal setting to ascertain what they know and how work through problems when they don’t know the answer.
c. Ask the applicant to read a statistically demanding document and then request a summary plus feedback from the candidate. This should also tell us something about what the candidate knows about statistics and whether they can summarize the relevant parts in a satisfactory manner.

Part 2: Technical Skills
To assess a candidates technical proficiency, here are some techniques that work well based on my previous experience.
a. Show an applicant some imperfect code that is unnecessarily long or could be improved. Ask them to look it over and provide their suggestions on how’d they do things differently.
b.Put together several small code snippets in various programming languages that the candidate may or may not know. Ask them to go through the code, identify what is happening at each step, and explain the final result.

The possibilities are endless, but there has to be better ways to assess the quality of candidates to analytics roles than the ‘homework assignment.’ In any case, I’ll be refusing to do any more assignments as a part of the hiring process.



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