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Data Scientists In Demand: Experian DataLab’s Eric Haller Weighs In

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Data-ScientistsTop science and math professionals working on a new frontier in IT are enjoying a great work/life balance, getting paid well, and getting hunted by recruiting firms. That new frontier? Data.

Data Scientists often make six figures, contribute to exciting new technologies, and work at highly rated companies. Recently, Glassdoor.com ranked Data Scientist as its “number one job” for young professionals. In October 2012, an article in Harvard Business Review called Data Science “the sexiest job of the 21st century” and talked about how, with a dearth of these skilled professionals available, companies are trying every trick in the book to get more of them on board, including developing their own Data Scientists in-house. Meanwhile, educational institutions are also trying to help out; universities such as North Carolina State and others are at work building Master of Science programs that would prepare a young careerist to swim in data. As for the Data Scientists themselves, HBR indicates that because of the choices available, “salaries will be bid upward.”

So what goes into building one of these awesome STEM careers?

Eric Haller is EVP at Experian DataLabs. Besides being one of America’s three credit agencies, Experian also maintains a host of other data services in the consumer and commercial markets. In the auto industry the company has built its own Carfax-type vehicle history reporting service, and in the rapidly advancing field of health care their footprint is growing at ever-increasing rates.

At three Experian DataLabs offices in Costa Mesa, California, London, and Sao Paolo, researchers work on “breakthrough experimentation” involving data applications.

“We operate outside of our product development processes,” Haller said in a recent interview, explaining how projects look at metrics and issues like “behavior of spend” to create new data services and processes.

Haller also had some words for would-be Data Scientists. First, Haller said there are many different points of entry to a Data Science career. While about 90% of the lab’s personnel have gone through the extensive process of earning a Ph.D. in a field like physics or math, some have no college degree at all. These “home grown” data gurus might earn their stripes in public competitions, or build Hadoop setups in their garages. They might graduate with a bachelor’s degree in economics or political science, but later find a “passion” for data.

What almost all of them have in common, Haller said, is the ability to code.

Many experts call coding the single most important skill for a Data Scientist; Haller didn’t disagree. One key piece of advice he had for aspiring pros was to take classes in Python or other portable languages, for example, on platforms like Coursera.

However, Haller added, there are other useful skills that make someone valuable as a Data Scientist. He talked about the many “disciplines” that go into working with data.

One example is the difference between those Haller called “Machine Learning people,” who might rely more heavily on algorithms to produce relatively raw results, and others who might lean toward the “clean up” and organization of data. With so much power built into new algorithmic technologies, some might be satisfied to build something big and keep it more abstract. It takes another type of effort to come in and label data and, as Haller says, make it more accessible to an outside auditor, or for that matter, deliver it to a customer.

Haller talks about a “learning curve” that involves learning not only how to manipulate data, but how to apply the results to an industry or a particular client firm.

He uses the example of a top Experian scientist meeting up with an analyst from a banking customer. To have a good conversation, both have to have an understanding of various aspects of the industry, such as, in the case of banks, risk.

Still, Haller said, the goal with a lot of data processes is to make them portable and applicable throughout a spectrum.

“We’re trying to do good things with data across many different markets,” Haller said.

Many of the technological challenges are similar across different industries, he said, but the jargon and the particular uses differ, and that’s one reason it takes so long for a Data Scientist newly out of college to become really conversant in what’s going on with the average project.

Going back to advice for the college crowd, Haller also talked about the value of collaboration.

“We don’t have any projects on our whiteboard right now that are just one individual,” Haller said, adding that the value of teamwork can also help newbies to get a foot in the door. While an internship can be gold, Haller said, it also makes sense for new STEM people to align themselves with a team, to pool resources, and prove their mettle together.

In terms of “specializations” and ways to stand out as a Data Scientist, Haller suggested that it’s good to have a broader skill set, rather than trying to compartmentalize skills. However, he conceded, it can make sense to showcase skills in regard to particular types of data, as one of the biggest considerations on any project involves precise handling of a certain data type. For instance, dealing with scrubbed, tabled database information is much different than looking through a rat’s nest of data cobbled together from different source points and presented in an unstructured way. And that doesn’t even address the different challenges of dealing with other types of raw data like image and video.

In the end, young professionals get a few very valuable tips here: work on coding skills, along with data problem-solving skills, and find some of those entry points that will be valuable in getting closer to a Data Scientist role.

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