Originally posted August 16, 2017. Updated May 6, 2019.
Every day humans collectively create the equivalent of 530,000,000 million digital songs or 250,000 Libraries of Congress worth of data. Data is quite literally everywhere. Every click, email, push notification, blog post, and Instagram upload is data. And that data is incredibly valuable.
For the vast majority of companies and organizations around the world, understanding and leveraging data is now a key business concern. But the problem with having so much data is that it takes a highly specialized skillset to tease out the insights and actionable intel locked up inside of potentially massive datasets.
That’s why data specialists have become one of the most highly sought after professionals on the talent market. Right now there is far more demand for data pros than supply -- IBM predicts that demand for data scientists will rise 28% by 2020. That’s one reason why the Harvard Business Review declared “data scientist” the sexiest job of the 21st century seven years ago.
In short, companies and organizations of all types need data scientists. Desperately. And if you don’t know what you’re doing you’re gonna get left in the dust. Here’s where to start.
According to UC Berkeley, data science is the practice of "identifying relevant questions, collecting data from a multitude of different data sources, organizing the information, translating results into solutions, and communicating their findings in a way that positively affects business decisions.” Basically, data science uses automated methods to analyze massive amounts of data to extract valuable knowledge and insight.
Artificial Intelligence (AI)
• Focus: Solving tasks that are easy for humans, hard for computers
• Tasks: Planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or biz transactions, creative work
Natural Language Processing (NLP)
• Focus: Processing interactions between humans and computers
• Tasks: Natural language generation and understanding, connecting language and machine perception, dialog systems
Machine Learning
• Focus: Figuring out the “correct” action without programmer’s help
• Tasks: Email filtering, detection of network intruders, optical character recognition, rank filing, computer vision
Deep Learning
• Focus: Learning tasks of artificial neutral networks containing more than 1 hidden layer
• Tasks: Computer vision, speech recognition, NLP, audio recognition, social network filtering, machine translation, bioinformatics
Recommendations
• Focus: Discovering patterns in datasets by learning and predicting consumers choices
• Tasks: Collaborative filtering, content-based filtering, demographic, knowledge-based
There are three primary data-focused roles: data scientists, data analysts, and data engineers. Here is how to differentiate between different data-focused roles and candidates.
Data Engineer
Data Analyst
Data Scientist
Recruiters, work closely with hiring managers to build out accurate job descriptions.
Iron out nuances to distinguish which types of data scientists will be the best fit for the business’ needs. Hone in on the skillset and experience of the type of data scientist you’re looking for.
Think long term. Understand how the org plans to leverage this role within the product roadmap.
Set realistic expectations of available candidate pool. There are more roles than candidates, so recruit accordingly.
Build a list of ideal candidates and calibrate with hiring manager to gauge fit against reality of talent market.
If you would like to dig deeper into how to effectively recruit data scientists, check out our free webinar for sample data scientist boolean search strings and other tips that can give you a leg up in the war for data talent. Also, Entelo's database has thousands of the world's best data scientists. If you'd like to know more about how it works, sign up for a free demo.
Related Articles:
How to Power Your Company Culture With Data and Feedback Loops
How to Hire Data Scientists When It Seems Like There Are None to Hire
What Recruiters Can Learn From Lifecycle Marketing