As the world is becoming awash in big data, demand is quickly growing for skilled workers who can help companies experiment with new ways to make decisions.

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As the world is becoming awash in big data, demand is quickly growing for skilled workers who can help companies experiment with new ways to make decisions using data.

Craig Reynolds, a principal and consulting actuary at Milliman in Seattle, says the breakout of data science in the life insurance world is biggest single trend he has seen in his 30-year career in insurance.

“But hiring can be a challenge; good people are highly in demand, especially in Seattle,” says Reynolds.

No longer are companies relying on a single data expert with a laundry list of skills to convert data into actionable insights. Instead, they’re hiring data teams comprised of people with various specializations that complement each other — opening up the field to people with a variety of backgrounds.

While new titles, such as data scientist, data analyst and data engineer, have recently emerged, these core roles have long existed.

“These job descriptions tend to reflect the background of the people that are already out there,” says Bill Howe, an associate professor in the Information School and the Computer Science and Engineering Department at the University of Washington.

Howe compares a data engineer role to that of a software engineer who also has a background in statistics, and a data scientist to a statistician with an expertise in computer programming. A data analyst aligns with a business analyst who has augmented their skill set with statistics and computer programming.

These positions commonly require a background in mathematics and statistics, along with experience in programming languages Python, SQL and R.

The job titles associated with big data are new enough that the U.S. Bureau of Labor Statistics (BLS) lumps them in the current classifications of statisticians, computer programmers or in other occupations, depending on the tasks. In the BLS occupational outlook, statisticians are projected to be in one of the fastest-growing fields, with a nearly 34 percent increase in statistician jobs projected from 2014 to 2024. The annual median salary of statisticians in 2014 was $79,990, according to the BLS.

Positions can vary widely between companies, but in general, data engineers tend to build databases, for which they need knowledge of any number of programming languages. Data scientists and data analysts then use these databases to process and make sense of the data, often with the data scientist building the models they both use.

This vast assortment of data jobs necessitates applicants focus on the job description rather than the title. And when reading the long lists of qualifications in the job posting, keep in mind skill sets often stretch across a broad range of general knowledge with a deep expertise in one area.

“Skills are often listed in order of importance,” says Edward Mabanglo, who works with data every day as a senior optimization analyst at Nordstrom. He points out that employers don’t expect applicants to have every single skill in their list of requirements.

“But if the stuff you have to bring to the table is at the bottom of the list, you have some work to do,” he adds.

Mabanglo suggests that when applying for jobs, candidates “make an inventory of skills, and build a story on how that will contribute right away to the company.”

This skills assessment can also help job seekers target what new knowledge they need to enter the field. Online courses and coding boot camps have become a popular way to expand proficiency in the field, varying between courses in specific software languages and tackling larger topics like artificial intelligence and computer security.

Enrolling in coding boot camps can not only boost skills, Mabanglo says, but also show future employers you are invested and serious about transitioning into a data career.

“There are many different paths into this field, so you don’t want to build a pipeline,” Howe says. “You want to allow people take their own path.”

He says many coding boot camps, designed to help people with advanced science and math degrees pivot their skills into data science, do a good job of exposing participants to a variety of software tools. But students starting from scratch who need a more in-depth education might get more out of a graduate school program.

As big data infiltrates almost every job, writing code will become a fundamental skill everyone needs to learn, Howe says.

“Whether you are going to be a journalist or an English major or historian, because of data, for questions about your own field you are going to apply algorithms to large sets of data,” Howe says. “It’s not just going to be about manually poring over old manuscripts in the library somewhere, it’s going to be computationally poring over lots and lots of data.”