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 five 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.
What do we mean when we say data science?
According to NYU, data science “is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.” Basically, data science uses automated methods to analyze massive amounts of data to extract valuable knowledge and insight.
Data science by the numbers:
- 16th highest paying job in demand
- Over 4,000 job openings nationwide
- Median base salary: $110k
- In 2020: 50x more data than in 2011
- By 2018: 4-5 million US jobs requiring data analysis skills
Where data science is particularly important:
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
• Focus: Figuring out the “correct” action without programmer’s help
• Tasks: Email filtering, detection of network intruders, optical character recognition, rank filing, computer vision
• 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
• Focus: Discovering patterns in datasets by learning and predicting consumers choices
• Tasks: Collaborative filtering, content-based filtering, demographic, knowledge-based
Common data science roles:
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.
- Typical majors: Computer science, engineering
- Tend to be in the middle of the group in terms of experience and compensation
- Average starting salary = $100k
- Average starting salary in the Bay Area = $115k-$125k
- Industries hiring data engineers: Any industry in which large amounts of data is collected and stored
- Typical majors: Business, economics, statistics
- Tend to be the least compensated group.
- Average starting salary = $65k
- Average starting salary in the Bay Area = $70k-$85k
- Industries hiring data analysts: Consulting, healthcare, banking
- Typical majors: Math, applied statistics, operations research, computer science, physics, Aerospace engineering
- Tend to be the highest compensated group with the most education and experience.
- Average starting salary = $115k
- Average starting salary in the Bay Area = $115k-$130k
- Industries hiring data scientists: Any industry in which large amounts of data is analyzed
How to effectively recruit data specialists:
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.