{"id":22881,"date":"2021-04-26T10:19:36","date_gmt":"2021-04-26T10:19:36","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/i-know-something-you-dont-know-how-not-to-job-interview\/"},"modified":"2023-08-24T11:56:15","modified_gmt":"2023-08-24T11:56:15","slug":"i-know-something-you-dont-know-how-not-to-job-interview","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/i-know-something-you-dont-know-how-not-to-job-interview\/","title":{"rendered":"I know Something You Don\u2019t Know\u200a\u2014\u200aHow Not To Job Interview"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"22881\" class=\"elementor elementor-22881\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-2eaca3b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2eaca3b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7630f25\" data-id=\"7630f25\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f46c04e elementor-widget elementor-widget-text-editor\" data-id=\"f46c04e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>How hard is hiring? With one sheet of paper and maybe a few hours of conversation, a company is supposed to assess how well someone could contribute hopefully thousands of hours of work.<\/p>\n<p>The hiring manager has to assess how well a prospect\u2019s skills match the job requirements, how quickly they could learn new skills, how well they might interact with their future coworkers, how much they would enjoy the job, and how well it would fit into their desired growth path, not to mention how well they might fit as the company\u2019s needs evolve.<\/p>\n<p>One might imagine that in the field of data science, where technical expertise can be evaluated objectively and the interviewers professionally trained to analyze facts, hiring might be relatively straightforward\u200a \u2014 \u200aless of an art and more of a science.&nbsp;My experience, as both interviewer and interviewee, could not be further from that description. I believe that if data scientists looked at their hiring records as they examined their classification algorithms, most would be embarrassed by their performance.<\/p>\n<p>I\u2019ve failed interviewees I should have passed, passed interviewees I should have failed. On the other side of the table, I believe I\u2019ve also been unreasonably rejected many times, though I say that with imperfect information and imperfect humility.&nbsp;<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2898c4b elementor-widget elementor-widget-heading\" data-id=\"2898c4b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What sorts of questions are causing these ineffective interviews? The most common are:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7fb03ce elementor-widget elementor-widget-text-editor\" data-id=\"7fb03ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Questions that test specific knowledge<\/li><\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ac2ba2a elementor-widget elementor-widget-text-editor\" data-id=\"ac2ba2a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Sure, knowledge is power\u200a \u2014 \u200ahow can you not assess how much a candidate knows? Assessing someone\u2019s knowledge level so you understand how much they might come in with is necessary. Where an interviewer errs is when they use knowledge as a proxy for ability.<\/p>\n<p>Data scientists nowadays are expected to cover an enormous breadth of knowledge, ranging from probability theory to infrastructure monitoring to natural language processing to database technologies. In addition, as a fairly new discipline, many data science practitioners enter from different domains, with many largely self-taught. A specific question about log-loss or regularization techniques might fall into an interviewee\u2019s knowledge gap on that day, particularly if it is more theoretical and doesn\u2019t crop up in day-to-day application. Even more insidiously, an interviewer might fault someone for not knowing a fact that they themselves know, without giving the same scrutiny to the converse, whether the interviewee knows facts that the interviewer doesn\u2019t.&nbsp;<\/p>\n<p>A takeaway \u200a\u2014\u200a if reading one Wikipedia article read would have dramatically altered the interview, it is not a well-structured interview.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-39f8129 elementor-widget elementor-widget-text-editor\" data-id=\"39f8129\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Brainteasers with a trick answer<\/li><\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aebb0d6 elementor-widget elementor-widget-text-editor\" data-id=\"aebb0d6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Brainteasers have an infamous association with software engineering interviews, especially at Google. But these questions have been dropped from standard Google interviews (<a href=\"https:\/\/qz.com\/378228\/google-is-over-those-ridiculous-brainteasers-but-some-employees-didnt-get-the-memo\" class=\"broken_link\" rel=\"noopener\">source<\/a>) because the company\u2019s data showed they were poor indicators of future employee success. Still, coding-related brainteasers continue to get asked, often with the premise that they \u201cshow how someone thinks through a problem.\u201d<\/p>\n<p>There is merit in evaluating whether a candidate knows fundamental computer science concepts, such as a stack or a binary search tree. Where the question devolves into a tortuous riddle is when a trick is required, requiring the interviewee to play \u201cGuess what I\u2019m thinking?\u201d These may be fun, but do they really assess how good an employee this person will make? At best brainteasers are a roundabout way to view someone\u2019s cognitive work, at worst they are infuriating.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fbca551 elementor-widget elementor-widget-text-editor\" data-id=\"fbca551\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Measuring effort, not talent, with a take-home assignment<\/li><\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25bd216 elementor-widget elementor-widget-text-editor\" data-id=\"25bd216\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Take-home assignments can be very revealing because they truly can emulate a work problem. Here is a real world problem where a candidate can show their solution and their code. However, the trap to avoid is handing out an assignment that takes too long. Those who are good at their job and motivated to keep learning will not have time to do a long assignment just to get an interview. Instead, this type of job interview process will attract those who hate their jobs and are willing to put in tremendous effort to leave. Effort is a good signal, but it is gained at the expense of potentially great, but busy, candidates. Make sure the take-home assignment is clean and clearly scoped to avoid losing good candidates.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b4bd202 elementor-widget elementor-widget-text-editor\" data-id=\"b4bd202\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Over-indexing on communication skills<\/li><\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dd8073 elementor-widget elementor-widget-text-editor\" data-id=\"6dd8073\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Some customer-facing roles require top communication skills and all data science roles require some. For many data scientists though, much of the job will be spent coding alone, sometimes solving technical problems that no one else ever learns about.<\/p>\n<p>In an interview, the candidate\u2019s communication skills are front and center, while their work abilities are not yet clear. On the job however, those skills to analyze <a href=\"http:\/\/www.experfy.com\/blog\/bigdata-cloud\/five-ways-your-data-strategy-can-fail\/\" target=\"_blank\" rel=\"noreferrer noopener\">data<\/a> may be more important than the communication skills. Thus, it is natural, post-interview, to weigh communication skills disproportionately. Especially for junior data scientists, communication skills can be coached on the job far more easily than data scientist skills.\u00a0<\/p>\n<p>\u200a\u2014\u200a\u2014&nbsp;<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebd5297 elementor-widget elementor-widget-heading\" data-id=\"ebd5297\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">With all these pitfalls to avoid, what questions should a good interviewer ask?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29aad90 elementor-widget elementor-widget-text-editor\" data-id=\"29aad90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>First, it depends on the context of the job opening. Some companies may require an urgent, specific need to be filled, and the new hire must contribute very quickly. In these cases, assessing their current knowledge state is more important, and the first bullet point above is less valid.&nbsp;<\/p>\n<p>In many other cases, however, companies want to find and nourish great talents over time. Here the starting point is less important than one\u2019s growth qualities. How good is the candidate at learning new subjects? How curious are they? How have they demonstrated resilience when their programs seemed hopelessly buggy?<\/p>\n<p>A good data scientist is likely unable to control their company\u2019s environment. If there was no need to do text modeling, the candidate will not have professional experience in natural language processing. Instead of focusing on where they lack experience, delve into what they have experienced. Ask the candidate to go through a model that they built. What were they solving for? Where did the data come from? How many algorithms did they consider? How was the model productionized? Did they test any theories or research? Any new topics to apply to this project?<\/p>\n<p>Some recruiting departments like to standardize their interviews to a script, often under the guise of equity. But candidates\u2019 experiences are full of inequity, and improvising questions off of their experience is the fairest way to assess their aptitude and work characteristics.<\/p>\n<p>With regard to productionizing, did they just shrug and plead that this was the domain of the data engineers? Did they provide informed opinions on their chosen architecture, or did they reveal a tendency to copy and paste? Did they truly understand why their model worked \u200a\u2014\u200a or didn\u2019t? And what are some areas they can self-identify as ones of weakness, that this next role potentially could help improve?<\/p>\n<p>Finding great data scientists is harder than ever as demand continues to climb and supply is slow to catch up. Hiring based on a laundry list of technologies and buzzwords will lead to frustrations. Seeking out those who have the makeup to grow and allowing them to flourish will be a win-win for interviewers and interviewees.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>How hard is hiring? With one sheet of paper and maybe a few hours of conversation, a company is supposed to assess how well someone could contribute hopefully thousands of hours of work. The hiring manager has to assess how well a prospect\u2019s skills match the job requirements, how quickly they could learn new skills,<\/p>\n","protected":false},"author":1139,"featured_media":22882,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[226,111,981,92],"ppma_author":[3821],"class_list":["post-22881","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-ai","tag-ai-amp-machine-learning","tag-hiring","tag-machine-learning"],"authors":[{"term_id":3821,"user_id":1139,"is_guest":0,"slug":"cal-lee","display_name":"Cal Lee","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/04\/li3-150x150.jpeg","user_url":"","last_name":"Lee","first_name":"Cal","job_title":"","description":"Cal Lee is a Machine Learning Engineer at Urbint using AI to identify threats to utility infrastructure. He has also worked in the construction, manufacturing and agricultural industries."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22881","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/1139"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=22881"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22881\/revisions"}],"predecessor-version":[{"id":31384,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22881\/revisions\/31384"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/22882"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=22881"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=22881"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=22881"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=22881"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}