BrainStation has announced an expansion of its New York operations, including a 35,000-square-foot campus and a multi-year investment of over $20 million.
Even before Amazon selected New York as one of its two HQ2 locations, the tech industry in the Big Apple was flourishing. And with an abundance of organizations launching or expanding operations in New York, a demand for data professionals is on the rise.
“Mad Men have been replaced by Math Men,” said Kevin Lee, the executive chairman and co-founder of the New York-based marketing firm Didit.
“Nearly all forms of data and analytics competencies are in demand, particularly the ones that include strategic awareness of how best to use the data and which of the data in a sea of data to look at.”
Here is a closer look at why data skills are in demand in New York.
The Tech Industry Continues to Grow
More than 7,500 tech companies now call New York home, powered by almost $40 billion of investment in the past five years.
Those companies employ 120,000 people – 60 percent more than a decade ago – and the momentum is only expected to continue, with a recent report from Accenture and Tech:NYC concluding that 80 percent of New York tech companies planned to expand hiring in 2018.
As we recently wrote about, a real startup ecosystem has also been established over the last few years, which bodes well for continued growth. In fact, the New York startup ecosystem is now valued at an estimated $71 billion, making it the third most valuable ecosystem in the world.
There is a Lack of Data Professionals
In the six years since the Harvard Business Review declared that Data Scientist was the sexiest job of the 21st century, data professionals have only become more attractive to employers. Bloomberg recently called data science America’s hottest job, reporting that “people with data science bona fides are among the most sought-after professionals in the business.”
In fact, a recent report from IBM determined that annual demand for Data Scientists, Data Developers, and Data Engineers would reach nearly 700,000 openings by 2020, with the number of jobs for all data professionals rising 39 percent to 2.72 million total positions.
It’s easy to understand why they’re so optimistic about the continued rise of data – the International Data Corporation’s 2017 forecast predicted revenues for big data and business analytics would reach $150.8 billion globally, a 12.4 year-over-year increase.
Another interesting wrinkle is just how quickly this happened. An August 2018 LinkedIn workforce report noted that just three years ago, there was a national surplus of data science professionals – now, the U.S. shortage has swelled to 151,717 people.
That shortage was “particularly acute” in New York, LinkedIn found – the Big Apple had a shortage of 34,032 data professionals, the most pronounced of any city in the country.
Those who do possess the right skill sets will be handsomely rewarded. Indeed found that Data Scientists bring home an average salary of $137,120 annually, nine percent above the national average, while Data Analysts average $80,113, which is 10 percent higher than the U.S. mean.
Data Roles are Hard to Fill
The IBM report found that Data Science and Analyst jobs were the hardest to fill, taking five days longer than average to find the right candidate.
“Organizations working on data science projects will find the hiring process longer and more difficult,” said Remy Rosenbaum, VP of Marketing with the New York-based strategic technology consulting and implementation firm Caserta.
“As a Data Science project typically requires a 2:1 ratio of Data Engineers to Data Scientists, experienced Data Engineers will prove harder to hire.”
Specific competencies can also correlate to higher compensation. From IBM’s findings, Data and Analytics professionals who have mastered MapReduce bring home an annual average income of $115,907, while experience with Apache Pig, Hive, and Hadoop similarly correlates to annual salaries north of $110,000. Looking for those skills can complicate the hiring process.
“Context also affects the degree of hiring difficulty,” Rosenbaum pointed out. “Take the statistical programming language R, for example. Despite being one of the most commonly requested Data Scientist skills, its rarity among Finance and Risk Analytics managers makes proficiency in R one of the highest-paying skills for this role.”
Given how acute the demand is for data professionals in New York, there are a number of other skills that are similarly sought-after. And the best candidates are the ones who can synthesize many of the in-demand data competencies.
“It’s not just about data and reporting,” Lee stressed. “It’s also about strategy around the right data to look for, and how to apply the learning that comes from good data analysis.”