Data Scientist: The Sexiest Job of the 21st Century

The Hot Job of the Decade

Hal Varian, the chief economist at Google, is known to have said, “The sexy job in the next 10 years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”

If “sexy” means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills.

Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.

One question raised by this is whether some firms would be wise to wait until that second generation of data scientists emerges, and the candidates are more numerous, less expensive, and easier to vet and assimilate in a business setting. Why not leave the trouble of hunting down and domesticating exotic talent to the big data start-ups and to firms like GE and Walmart, whose aggressive strategies require them to be at the forefront?

The problem with that reasoning is that the advance of big data shows no signs of slowing. If companies sit out this trend’s early days for lack of talent, they risk falling behind as competitors and channel partners gain nearly unassailable advantages. Think of big data as an epic wave gathering now, starting to crest. If you want to catch it, you need people who can surf.

Where Do You Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.

Below are the average base salaries for the following positions: 7

Data analyst: $65,470

Data scientist: $120,931

Senior data scientist: $141,257

Data engineer: $137,776

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.