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<\/a><\/p>\nChris Pearson is a Co-Founder of Big Cloud<\/a>\u00a0specialist recruiters in the fields of Big Data & Data Science.\u201d. In his debut article with us, Chris talks about the science behind finding the right Data Scientist.<\/p>\n Read any Big Data Gartner report or salary survey and they all tell you the same thing, Data Scientists are the MVP\u2019s in the team.\u00a0<\/span>So how have these self-dubbed \u201csuper geeks\u201d managed to go from the receiving end of scrunched up paper snowballs in the classroom to becoming the High School Jock or the Prom Queen?<\/span><\/p>\n Well that\u2019s pretty simple, it\u2019s a case of supply and demand.<\/p>\n Most CIO\u2019s who are on their journey into the world of Big Data all want to hire the best Data Scientists for their business.\u00a0 And from speaking to a fair few of these Data Scientists, it\u2019s pretty clear to see why.\u00a0 Some are making or saving businesses $multi-millions.\u00a0 Some are curing diseases and ridding areas of famine.\u00a0 Some are even predicting the winners at the Oscars.<\/p>\n If Gotham City was a real place, Batman would probably be in line at the Welfare Office, as some Data Scientists are now predicting where crime is about to happen!<\/p>\n So, it\u2019s pretty clear to see why these people are held in the highest regard and clear to see why most businesses are clambering over themselves to hire the best in show.\u00a0 In some instances this has become a bun-fight at the soup kitchen.\u00a0 According to EMC, 65% of Data Scientists believe that demand for their skills will outweigh availability over the next 5 years.\u00a0 Frightening to think that the pendulum could swing that far.<\/p>\n It\u2019s easy to see why businesses want to hire a good Data Scientist.\u00a0 Attracting the right one is definitely the hard part.<\/p>\n Some traditional methods of recruitment can be ruled out straight away.\u00a0 Job boards can prove to be futile, such is the shortage of talent available.\u00a0 Even if the best Data Scientist was to make themselves known on a job board, the rush of approaches from your competitors and other recruiters to hire them, could make the challenge of getting your hands on a ticket for the World Cup final, seem like a walk in the park.<\/p>\n If you know where Data Scientists tend to \u2018hang out\u2019 online on social media and other forums, advertising can be successful.\u00a0 However, in my experience, unless your company has a following of people in the Data Science industry or you have a brand like Twitter, Google or Facebook and have queues of suitable applicants at the door, advertising on your own website alone can be a waste of time.\u00a0 Advertising can also be quite costly, with no guarantee of a return on investment and it can leave you with an inbox full of applicants who just aren\u2019t relevant.<\/p>\n Being a recruiter in this field, it would be really easy of me to say that the best method is to use a recruitment company, however, the typical feedback we get from new clients or candidates when asked about their last recruiter is that they \u201cdidn’t\u00a0come back with any relevant candidates\/jobs\u201d.<\/i>\u00a0 Big Data is the biggest band wagon rolling through \u2018Recruitmentville\u2019 right now, so it might be worth testing their credentials to see how successful they\u2019ve been, before you start agreeing to pay fees.<\/p>\n Good old fashioned networking can be a trusted route to finding that prized Data Scientist.\u00a0 According to the most recent Adler Study, around 83% of LinkedIn users (out of 225m) are \u2018passive jobseekers\u2019, meaning that they may not be actively looking, but would be interested to hear about new opportunities.\u00a0 These are the people who aren\u2019t updating their CV\u2019s to upload onto a job board.\u00a0 These aren\u2019t the people reading job adverts.\u00a0 These definitely aren\u2019t the people picking up the phone and introducing themselves to businesses!\u00a0 Typically, the best way of getting to them is through understanding your market and mapping out the talent within it.\u00a0 Mapping out who works for your competitiors and even going as far as mapping out your \u2018competitor\u2019s competitors\u2019.\u00a0 The only down side to this is that it can be time consuming and may not actually lead to a hire.<\/p>\n It\u2019s worth pointing out that if you already have a team of Data Scientists, don\u2019t let the good ones go!\u00a0 We speak with Data Scientists all the time and within the first minute of conversation, it\u2019s easy to understand why they\u2019re looking for something new.\u00a0 They want me to find them a challenging role, so why aren\u2019t you pushing and developing them enough in your business?<\/i>\u00a0 They want me to find them more money, so why don\u2019t you know that they\u2019re unhappy with their current deal?<\/i>\u00a0 They want me to find them a job with more seniority, so why aren\u2019t you providing them with a clear enough career path?<\/i>\u00a0 Sometimes the easiest and cheapest recruitment, is recruiting the team you already have.<\/p>\n Hopefully this will give you some options when it comes to hiring that next Data Scientist.\u00a0 However, I couldn\u2019t wrap this article up without highlighting the biggest reason that prevents businesses from hiring the right people in this area\u2026\u2026\u201dwhy do I want to hire a Data Scientist, again?\u201d<\/i><\/p>\n <\/p>\n
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