{"id":1602,"date":"2019-03-28T03:28:11","date_gmt":"2019-03-28T03:28:11","guid":{"rendered":"http:\/\/kusuaks7\/?p=1207"},"modified":"2023-08-14T09:46:24","modified_gmt":"2023-08-14T09:46:24","slug":"to-get-hired-as-a-data-scientist-dont-follow-the-herd","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/to-get-hired-as-a-data-scientist-dont-follow-the-herd\/","title":{"rendered":"To get hired as a data scientist, don\u2019t follow the herd"},"content":{"rendered":"

I still remember the moment my brother decided to sell his bitcoin. It was 2017, and we were at a Starbucks. We were approached by a middle-aged woman who was giving away pamphlets to anyone who would take one. \u201cBITCOIN: a path to early retirement\u201d was written in bold font at the top.<\/p>\n

I was curious, so I asked her what she thought of the cryptocurrency market more generally, but it turned out that she knew of no other cryptocurrency besides bitcoin. Ethereum? \u201cNever heard of it.\u201d Litecoin? \u201cThat\u2019s the cheap version of bitcoin, right?\u201d<\/p>\n

Now, as a rule of thumb, when even the clueless middle aged lady at the local Starbucks is pitching you on the latest tech trend, you\u2019re probably approaching peak hype. Or, if you prefer, a \u201cbubble\u201d.<\/p>\n

This isn\u2019t a new observation, of course. Everyone agrees that when it comes to investing, if you\u2019re doing what everyone else is doing, you\u2019re unlikely to see any returns. What\u2019s weird though, is that people fail to apply this same reasoning when it comes to investing\u00a0in themselves<\/em>.<\/p>\n

Let\u2019s suppose you want to get hired as a data scientist. If you\u2019re doing all of the standard \u201cI want to become a data scientist\u201d things, then this means you shouldn\u2019t expect to land your dream job. The market is currently full of junior talent, and as a result, the median aspiring data scientist is unlikely to get much traction. So if you want to avoid the median outcome, why do median things?<\/p>\n

The problem is, most people don\u2019t think this way when they embark on their data science journeys. I\u2019ve spoken to literally hundreds of aspiring data scientists through my work at\u00a0SharpestMinds<\/a>, and about 80% of them have roughly the same story to tell:<\/p>\n

    \n
  1. First, they learn the ropes (Python + sklearn + Pandas + maybe some SQL or something)<\/li>\n
  2. Then, they take a cookie-cutter massive open online courses (MOOC) of some sort<\/li>\n
  3. They read a few job descriptions, get worried that they don\u2019t have what it takes<\/li>\n
  4. Maybe take another MOOC, maybe start applying to jobs through a jobs board<\/li>\n
  5. Hear nothing back (or at best, bomb a few interviews)<\/li>\n
  6. Get frustrated, think about doing a Master\u2019s, apply to some more jobs<\/li>\n
  7. Come to a decision point: do I repeat #2 through #7 until something different happens?<\/li>\n<\/ol>\n

    If this ever happens to you, odds are you\u2019re in a self-improvement bubble too: you\u2019re doing what everyone else is doing, but expecting a different outcome. The very first thing you need to do is\u00a0stop<\/em>.<\/p>\n

    If you want above-average outcomes, you can\u2019t do average things. But to avoid doing average things, you need to know what the average things are.<\/p>\n

    Here are some examples: if you needed to do a MOOC to learn the ropes, that\u2019s fine. But don\u2019t get stuck in a MOOC spiral: MOOCs are, almost by definition, designed for the average person, so you won\u2019t become an outstanding candidate by doing more of them. Likewise, if you have 4 or 5 Jupyter notebooks featuring the same boring sklearn\/Pandas\/seaborn\/Keras stack on your GitHub,\u00a0do not make another one<\/em>.<\/p>\n

    Overall, the rule is: if something seems like an obvious next step because everyone else is doing it, that\u2019s a great thing to\u00a0not do<\/em>. And conversely, you need to find the things that no one else is doing, and do those things as soon as possible.<\/p>\n

    What are those things? Based on what I\u2019ve seen, about 5 come to mind:<\/p>\n

      \n
    1. Replicate papers.<\/strong>\u00a0This is especially true if you\u2019re a deep learning buff. People don\u2019t do this because it\u2019s harder than grabbing a dataset and using a simple ANN or XGBoost to do cookie-cutter classification. Find the most interesting paper (ideally a relatively recent one) relevant to your field on the arXiv, and read it. Understand it. Then, replicate it, potentially on a new dataset. Write a blog post about it.<\/li>\n
    2. Don\u2019t get comfortable in your comfort zone.\u00a0<\/strong>If you start a new project, it had better be to learn some new frameworks\/libraries\/tools. If you\u2019re building your 6th Jupyter notebook that starts with\u00a0df = pd.read_csv(filename)\u00a0and ends with\u00a0f1 = f1_score(y_true, y_pred)\u00a0, it\u2019s time to change your strategy.<\/li>\n
    3. Learn boring things.<\/strong>\u00a0Other people aren\u2019t doing this because no one likes boring things. But learning a proper Git flow, how to use Docker, how to build an app using Flask, and how to deploy models on AWS or Google Cloud, are skills that companies desperately want applicants to have, but that are under-appreciated by a solid majority of applicants.<\/li>\n
    4. Do annoying things.<\/strong>\u00a01) Offer to present a paper at a local data science meetup. Or, at the very least, attend the local data science meetup. 2) Send cold messages to people on LinkedIn. Try to offer value upfront (\u201cI just noticed a typo on your website\u201d). DO NOT ASK THEM FOR A JOB RIGHT AWAY. Make your ask as specific as possible (\u201cI\u2019d love to get your feedback on my blog post\u201d). You\u2019re trying to build a relationship and expand your network, and that takes patience. 3) Attend conferences and network. 4) Start a study group.<\/li>\n
    5. Do things that seem crazy.<\/strong>\u00a0Everyone goes to the UCI repository, or uses some stock dataset (yawn) to build their project. Don\u2019t do that. Learn how to use a web scraping library, or some under-appreciated API to build your own, custom dataset. Data is hard to come by, and companies often need to rely on their engineers to get it for them. Your goal should be to come across as the kind of data science-obsessed lunatic who will build your own goddamn dataset if that\u2019s what it takes to get the job done.<\/li>\n<\/ol>\n

      Each of these strategies is another way to stand above the noise that recruiters face every day. None of them are silver bullets, but they are surefire ways to get more traction on the data science job market, and become a more capable data scientist.<\/p>\n

      At the end of the day, remember that when you\u2019re building your skills, you\u2019re investing in yourself. And that means that all the same economic principles that apply to investment apply here: if you want an outstanding outcome, you have to do outstanding things.<\/p>\n","protected":false},"excerpt":{"rendered":"

      If you’re doing all of the standard “I want to become a data scientist” things, then this means you shouldn’t expect to land your dream job. The market is currently full of junior talent, and as a result, the median aspiring data scientist is unlikely to get much traction. So if you want to avoid the median outcome, why do median things? The problem is, most people don’t think this way when they embark on their data science journeys.<\/p>\n","protected":false},"author":251,"featured_media":4255,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[2882],"class_list":["post-1602","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":2882,"user_id":251,"is_guest":0,"slug":"jeremie-harris","display_name":"Jeremie Harris","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Harris","first_name":"Jeremie","job_title":"","description":"Jeremie Harris is Co-Founder at SharpestMinds<\/a> that finds new grads their first jobs in machine learning and data science. He has many publications to his credit."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1602","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\/251"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1602"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1602\/revisions"}],"predecessor-version":[{"id":30193,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1602\/revisions\/30193"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4255"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1602"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1602"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1602"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1602"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}