{"id":193,"date":"2020-08-07T14:47:12","date_gmt":"2020-08-07T14:47:12","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/08\/07\/the-uncommon-data-science-job-guide\/"},"modified":"2020-08-07T14:47:12","modified_gmt":"2020-08-07T14:47:12","slug":"the-uncommon-data-science-job-guide","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/08\/07\/the-uncommon-data-science-job-guide\/","title":{"rendered":"The Uncommon Data Science Job Guide"},"content":{"rendered":"
\n

By Frederik Bussler<\/a>, Growth Marketing at Apteo<\/b>.<\/p>\n

<\/p>\n

Photo by\u00a0Jake Kokot<\/a>\u00a0on\u00a0Unsplash<\/a>.<\/em><\/p>\n

Getting a data science job is as easy as learning skills like Python and Jupyter, competing in Kaggle competitions, getting certified, and submitting your CV on job portals, right?<\/p>\n

Wrong.<\/strong><\/p>\n

Recently, Kaggle hit\u00a05 million<\/a>\u00a0unique users. Looking at other communities, Towards Data Science gets\u00a020 million<\/a>\u00a0monthly views. AI researcher Andrew Ng has\u00a04 million<\/a>\u00a0learners across his Coursera courses.<\/p>\n

In the meantime, there are just\u00a017,100<\/a>\u00a0ML Engineers worldwide. As of writing this article, there are\u00a02,100<\/a>\u00a0open<\/em>\u00a0Machine Learning Engineer roles on LinkedIn, worldwide. Of those, around\u00a080<\/a>\u00a0are at the FAANGs.<\/p>\n

<\/p>\n

\u201cOpen Data Science Roles\u201d by Apteo\u2019s\u00a0\u201cdata science career trends\u201d dashboard<\/a>.<\/em><\/p>\n

To be fair, if we include general data science and AI researcher roles, there are\u00a086,000<\/a>\u00a0total open positions worldwide. Nonetheless, calling data science \u201ccompetitive\u201d would be the understatement of the year.<\/p>\n

<\/p>\n

Data science communities vs. data science job opportunities. Visualized by author.<\/em><\/p>\n

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Red Ocean vs. Blue Ocean Strategy<\/h3>\n

\u00a0<\/p>\n

In business, a competitive field is referred to as a \u201cred ocean,\u201d where sharks are battling it out in a crowded space. In contrast, a \u201cblue ocean\u201d refers to unexplored, competitor-free market spaces.<\/p>\n

<\/p>\n

Icons by\u00a0Freepik<\/a>\u00a0from\u00a0flaticon.com<\/a>. Edited by author.<\/em><\/p>\n

As a prospective data scientist, your goal is to compete in the blue ocean. Here are four ways to do so.<\/p>\n

<\/p>\n

Red Ocean vs. Blue Ocean strategies. Created by author.<\/em><\/p>\n

\u00a0<\/p>\n

1. Use Niche Platforms and Communities<\/h3>\n

\u00a0<\/p>\n

I\u2019m a LinkedIn superfan. In fact, I\u2019ve been\u00a0featured by LinkedIn<\/a>\u00a0after taking 100+ LinkedIn learning courses, I\u2019ve made posts with\u00a0over 10,000 comments<\/a>, and I have over\u00a020,000 connections<\/a>. That being said, relying solely on LinkedIn is a red ocean strategy (\u26a0\ufe0f).<\/p>\n

<\/p>\n

LinkedIn post, by LinkedIn.<\/em><\/p>\n

LinkedIn boasts around\u00a0two-thirds of a billion<\/a>\u00a0users. That means if you solely network on LinkedIn, and apply for jobs through LinkedIn, you\u2019re competing with an\u00a0incredible\u00a0<\/em>amount of people.<\/p>\n

As someone who has used LinkedIn to hire people, good job postings can get flooded with so many applicants, so quickly, that it\u2019s hard to even skim through all the applications, let alone give feedback to everyone.<\/p>\n

That\u2019s why,\u00a0in addition<\/em>\u00a0to\u00a0<\/em>LinkedIn, I\u2019d recommend that you also use niche platforms, like\u00a0Shapr<\/a>, Y Combinator\u2019s\u00a0Work At A Startup<\/a>,\u00a0Lunchclub.AI<\/a>\u00a0(note: this is my invite link, but I don\u2019t get paid), Slack communities like\u00a0Wizards<\/a>, as well as offline communities on Meetup or Eventbrite.<\/p>\n

All of these are free.<\/p>\n

Tight-knit communities make it far easier to stand out. Shapr and Lunchclub, for instance, are professional networks completely focused on creating one-on-one connections.<\/p>\n

\u00a0<\/p>\n

2. Network to Avoid the \u201cBlack Hole Effect\u201d<\/h3>\n

\u00a0<\/p>\n

I once met a conference-goer who joked that \u201cnetworking is the new way of saying not-working.\u201d As amusing as that was, I also met a new client at that event.<\/p>\n

Networking is similar to the first point, but here I\u2019m referring explicitly to when it\u2019s time to apply for a job.<\/p>\n

<\/p>\n

Photo by\u00a0Chuttersnap<\/a>\u00a0from\u00a0StockSnap<\/a>.<\/em><\/p>\n

You\u2019ve probably heard stories of people submitting\u00a0hundreds\u00a0<\/em>of job applications and not getting a peep back. Maybe you\u2019ve been a victim of that phenomenon yourself.<\/p>\n

While some people do land jobs this way, it\u2019s increasingly likely that submitting your CV on some job portal means you\u2019ll never get a reply back. As I mentioned, hiring managers are getting absolutely flooded with applicants.<\/p>\n

It\u2019s like tossing your CV in a black hole.<\/p>\n

\n

Ask yourself: If you had to decide who to hire, between a complete stranger, and someone whom you were introduced to, who would you pick?<\/p>\n<\/blockquote>\n

As a result, hiring managers and executives almost always go with the applicant that they have\u00a0some<\/em>\u00a0connection with, even if it\u2019s just an introduction by a mutual acquaintance.<\/p>\n

The more you network, the more mutual connections you\u2019ll have with potential employers, and the easier it\u2019ll be to get an introduction.<\/p>\n

<\/p>\n

LinkedIn \u201cmutual connections.\u201d By author.<\/em><\/p>\n

Here\u2019s a super simple template you could use to ask a mutual connection for an intro:<\/p>\n

\n

First, engage with their latest posts, then message.<\/p>\n

Hey Connector_name,<\/em><\/p>\n

I hope you\u2019re doing well. Since we\u2019re in the same industry and have a mutual connection with [@name] at [@company], I was hoping you could introduce me over LinkedIn. I recently applied for their [job posting title].<\/em><\/p>\n

I wrote a draft message you can copy\/paste to them for an easy intro:<\/em><\/p>\n

Hello [@name],<\/em><\/p>\n

I noticed you\u2019re hiring for a [job posting title].<\/em><\/p>\n

I\u2019d like to introduce you to [@ Frederik Bussler] as a potential candidate, who has [achievement_1] and [achievement_2] in the space. Frederik\u2019s interested in speaking with you about the position. Would you like a quick intro?<\/em><\/p>\n

Thank you,<\/em><\/p>\n

Connector_name<\/em><\/p>\n<\/blockquote>\n

\u00a0<\/p>\n

3. Specialize<\/h3>\n

\u00a0<\/p>\n

<\/p>\n

Data Science Venn Diagram, created by author.<\/em><\/p>\n

Data science is a multidisciplinary field, and a large component is domain expertise.<\/p>\n

For example, Walmart\u00a0uses predictive models<\/a>\u00a0to anticipate demand at certain hours. If a hiring manager for a data scientist role had to choose between a \u201cPython expert\u201d and an \u201cexpert in predictive modeling for retail,\u201d obviously the more specialized candidate would win, all else being equal.<\/p>\n

Amazon\u2019s\u00a0recommendation engine<\/a>\u00a0is responsible for up to\u00a035% of Amazon\u2019s revenue<\/a>, and they\u2019re constantly hiring data science talent to grow this golden goose. If you\u2019ve worked on recommendation engines \u2014 even just as a side project \u2014 that\u2019d give you an edge over a more generalist applicant.<\/p>\n

Learning\u00a0specialized\u00a0skills \u2014 which speciality depends on your personal interests \u2014 is a game-changer.<\/p>\n

\u00a0<\/p>\n

4. Practical Projects > Certificates<\/h3>\n

\u00a0<\/p>\n

<\/p>\n

Certificates are all the rage in 2020. If you use LinkedIn, you\u2019ve surely noticed the infamous \u201ccertification post.\u201d<\/p>\n

Users show off their certificates from Coursera, EdX, online-learning portals, LinkedIn Learning, and a million other sources. I\u2019m guilty of this as well \u2014 as I mentioned, I\u2019ve taken 100+ LinkedIn Learning courses, and received certificates\u00a0from almost every Ivy League<\/a>.<\/p>\n

That being said, relying on certificates is a red ocean strategy (\u26a0\ufe0f). When millions of people have the same certificates as you, then you need differentiators.<\/p>\n

<\/p>\n

Comparing data science jobs and Andrew Ng\u2019s Coursera students. Created by author.<\/em><\/p>\n

Practical projects, where you analyze data that you\u2019re interested in, will give you a massive edge.<\/p>\n

For inspiration, you can look to the\u00a0data science career trends<\/a>\u00a0dashboard mentioned above and other\u00a0public workspaces<\/a>.<\/p>\n

\u00a0<\/p>\n

Conclusion<\/h3>\n

\u00a0<\/p>\n

Data Science is an increasingly competitive field, but you can stand out by using niche platforms, growing your professional network, specializing in an area that interests you, and sharing unique projects with the world.<\/p>\n

Original<\/a>. Reposted with permission.<\/p>\n

Related:<\/b><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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