Has geek chic gone wild? After all, Harvard Business Review famously declared that data scientist is “the sexiest job of the 21st century.” The most powerful are ranked by well-known publications and the U.S. government just hired its first official US Chief Data Scientist.

And if you think “sexy” equals expensive, you’d be right. Companies are spending a lot of money on data scientists – with starting salaries rising fast and strong demand for a scarce supply of talent forcing them up quickly.

That’s why companies must understand exactly why they need data scientists, what they should do and how their unique skills and knowledge can help the business.

What Data Scientists do and who's hiring them

Most data scientists have advanced degrees and training in math, statistics and/or computer science. Most likely they have experience in data mining, data visualization and/or information management. Previous work with cloud computing, infrastructure design and data warehousing is also fairly common. On a personal level, they are highly curious and passionate about problem solving.

Any company, in any industry, that crunches large volumes of numbers, possesses lots of operational and customer data, or can benefit from social media streams, credit data, consumer research or third-party data sets can benefit from having a data scientist or a data science team.

The Role of a Big Data Scientist in a Big Data, Data-Driven Culture

What a Data Scientist does all day

Put simply, data scientists apply powerful tools and advanced statistical modeling techniques to make discoveries about business problems, processes and platforms. But, let’s be clear: big data is not a “science project.” Rather it must be operationalized in specific ways – through more personalized offers to customers and prospects, better insight into pricing trends and closer tracking of customer behaviors across channels. However, to do those things more effectively and efficiently, at larger scale and with more precision, requires that someone continuously seek the leading edge in terms of performance and constantly rethink what’s possible with big data.

That someone is usually a data scientist. They are the ones experimenting with intelligence-gathering technologies, developing sophisticated models and algorithms and combining disparate data sets. They will ask the biggest most improbable seeming questions. They will lead the deepest data-diving expeditions and boldest explorations into the largest and most diverse data sets. They will seek the black swans lurking in your data streams. Or maybe just help you identify the whiskies you might like best.

So they will be ones at the forefront of:

  • Linking into new and different data streams to more precisely offer products and services to consumers and find the deepest causalities in customer behavior
  • Using sensor data to detect weather patterns and reroute the supply chains
  • Uncovering fraud by finding anomalies in operational data or market patterns
  • Advancing the speed at which certain data sets can be accessed, analyzed and integrated
  • Identifying the most innovative ways to use the Internet of Things.

One important caveat: companies must make sure data scientists focus on solving big problems and generating big ideas, not on tasks and functions that can be automated (such as running churn analytics or sentiment analysis). Yes, data scientists can have a look at these areas and look for deep patterns, but they must be focused on bigger-picture and potentially groundbreaking opportunities.

Identifying the Business Beyond the Science

Identifying the business beyond the science

Yes, there is an aspect of the laboratory in data science. The scientific method of inquiry, hypothesis and validation are how data scientists will redefine the possible for the business. It’s R&D focused on disruptions, enabled by data, and directly aligned to enterprise goals and strategies.

But for investments in a data scientist to pay off, they must have other skills, too. According to one industry observer:

What we need are data scientists who bring more to the table than just mathematics and code. We need to find the people who can make data a thread that runs through the entire fabric of the organization.

That requires strong communications and collaboration skills (and playing well with others). Real leadership skills and the ability to evangelize (about the use of strong data practices, for instance) are added bonuses. There’s an increasing awareness that more creativity is also needed in the data science community, that data artists might even be the next sexiest job.

Data scientists bring a critical set of skills companies need to win with big data, but it’s just one set, that must be complemented by executive sponsors, marketing big data experts and business analysts, each of which have similarly important roles to play.