2017 Data Science Job Market Outlook

Introduction

Data science is one of the hottest fields to work in today, and it’s only getting hotter as more companies realize that data can help them make better decisions. The demand for data scientists has been on the rise for years now, and this trend shows no sign of slowing down. According to a recent report from Indeed Hiring Lab , there are almost three times more open jobs for data scientists right now than there were just five years ago! But despite all this demand, finding qualified candidates can still be tough—especially if you’re looking at experienced professionals who have years of experience working with big data sets. That’s why we want to help you find the best candidates out there by breaking down some key trends in hiring practices and salaries so you can determine whether or not your current resources will suffice going forward.

The data science job market is on the rise

Data science jobs are on the rise, and it’s not just a fad. The demand for data scientists is real and growing, with an estimated increase of 12{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} from 2016-2020 according to the U.S Bureau of Labor Statistics (BLS). Data scientists make an average salary of $116K per year–that’s more than twice what software engineers get paid!

Top skills for data scientists

As you might expect, the top skills for data scientists vary depending on which niche you’re looking at. For example:

• Python is a must-have skill for machine learning and data science professionals. It’s also a great language to learn if you want to get into big data analytics or even start your own company.
• R is another common programming language used by statisticians and data miners who work with large datasets (particularly those generated from social media).
• SQL stands for “Structured Query Language,” which is basically just an industry standard way of accessing databases through code rather than ordinary queries like SELECT * FROM TABLE WHERE ID = 1; instead of typing this every time, we can just write SELECT * FROM TABLE WHERE ID = 1; in our code instead! This makes it easier for developers who are new at coding because they don’t need any special knowledge about databases before starting out–they can just focus on writing good algorithms instead

Data scientist salaries and the demand for experienced candidates

Data scientist salaries vary by company and location, but the demand for experienced candidates is high. Data scientists with five to 10 years of experience can command salaries as high as $250,000 per year. In addition to compensation, employers offer perks like flexible work schedules and free food in order to attract top-notch talent.

In addition to a competitive salary, data scientists are rewarded with other benefits such as flexible work schedules and free food (or beer on Fridays).

Hiring managers are increasingly looking for a balance of technical expertise and business knowledge

If you’re a data scientist, it’s time to brush up on your business skills.

Data scientists are increasingly expected to have an understanding of how their work fits into the bigger picture of their organization and its goals–and that means communicating with non-technical people.

Business knowledge is important because it helps data scientists understand what is important to the business and what questions they should ask in order to get useful answers from their models or algorithms. It also makes them better at explaining their findings clearly so that decision makers can make decisions based on sound statistical evidence rather than hunches or gut feelings (which often lead us astray).

The most in-demand software tools and platforms are changing

The most in-demand software tools and platforms are changing. The rise of Python is a sign that companies are looking for more agile languages that can be used for data science tasks as well as business intelligence and machine learning applications. With its rapid development cycle, Python has become the most popular language for big data projects.

SQL, however, remains an essential tool for data analysis and business intelligence applications. Spark SQL (or Apache Spark) was introduced by Databricks in 2014 to provide an API on top of Apache Spark’s Resilient Distributed Datasets (RDDs), enabling developers to create SQL queries against these datasets without having to write any Java code or operate at the level at which they were built (which would be very low).

Hadoop continues its reign as king of big data technologies despite recent competition from newer technologies like Apache Storm or Kafka; however it faces stiffer competition from other tools such as Elasticsearch due to their ability offer better performance at lower cost when compared against traditional Hadoop clusters

Companies need to invest in training their people if they want to keep up with the demand.

To keep up with the demand, companies will need to invest in training their people. They’ll also have to hire more people and find ones who have the right skills. They’ll need new tools and platforms for data scientists too–not just those who can write code but also those who can communicate effectively with non-technical stakeholders. The long-term solution is likely an investment in new technologies that automate some of these processes so that humans aren’t doing all of this work manually anymore (and then there’s always AI).

Conclusion

As the demand for data scientists continues to rise, companies will need to invest in training their people if they want to keep up with it. This means investing in both technical skills and business knowledge so that their employees can work effectively with a variety of stakeholders across departments. It also means investing in hiring better talent from outside sources if internal resources are insufficient or unavailable at all times