Data Scientist: The Sexiest Job of the 21st Century 02-21

Data Scientist: The Sexiest Job of the 21st Century

When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up. The company had just under 8 million accounts, and the number was growing quickly as existing members invited their friends and colleagues to join. But users weren’t seeking out connections with the people who were already on the site at the rate executives had expected. Something was apparently missing in the social experience. As one LinkedIn manager put it, “It was like arriving at a conference reception and realizing you don’t know anyone. So you just stand in the corner sipping your drink—and you probably leave early.”
Goldman, a PhD in physics from Stanford, was intrigued by the linking he did see going on and by the richness of the user profiles. It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users. But LinkedIn’s engineering team, caught up in the challenges of scaling up the site, seemed uninterested. Some colleagues were openly dismissive of Goldman’s ideas. Why would users need LinkedIn to figure out their networks for them? The site already had an address book importer that could pull in all a member’s connections.

Luckily, Reid Hoffman, LinkedIn’s cofounder and CEO at the time (now its executive chairman), had faith in the power of analytics because of his experiences at PayPal, and he had granted Goldman a high degree of autonomy. For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages.

Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know—for example, people who had shared their tenures at schools and workplaces. He did this by ginning up a custom ad that displayed the three best new matches for each user based on the background entered in his or her LinkedIn profile. Within days it was obvious that something remarkable was taking place. The click-through rate on those ads was the highest ever seen. Goldman continued to refine how the suggestions were generated, incorporating networking ideas such as “triangle closing”—the notion that if you know Larry and Sue, there’s a good chance that Larry and Sue know each other. Goldman and his team also got the action required to respond to a suggestion down to one click.

The shortage of data scientists is becoming a serious constraint in some sectors.

It didn’t take long for LinkedIn’s top managers to recognize a good idea and make it a standard feature. That’s when things really took off. “People You May Know” ads achieved a click-through rate 30% higher than the rate obtained by other prompts to visit more pages on the site. They generated millions of new page views. Thanks to this one feature, LinkedIn’s growth trajectory shifted significantly upward.

A New Breed

Goldman is a good example of a new key player in organizations: the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data. The title has been around for only a few years. (It was coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook.) But thousands of data scientists are already working at both start-ups and well-established companies. Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.

Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization. While those are important breakthroughs, at least as important are the people with the skill set (and the mind-set) to put them to good use. On this front, demand has raced ahead of supply. Indeed, the shortage of data scientists is becoming a serious constraint in some sectors. Greylock Partners, an early-stage venture firm that has backed companies such as Facebook, LinkedIn, Palo Alto Networks, and Workday, is worried enough about the tight labor pool that it has built its own specialized recruiting team to channel talent to businesses in its portfolio. “Once they have data,” says Dan Portillo, who leads that team, “they really need people who can manage it and find insights in it.”

Who Are These People?

If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of those tasks is as straightforward as it is with other, established organizational roles. Start with the fact that there are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.

The first step in filling the need for data scientists, therefore, is to understand what they do in businesses. Then ask, What skills do they need? And what fields are those skills most readily found in?

More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.

Data scientists realize that they face technical limitations, but they don’t allow that to bog down their search for novel solutions. As they make discoveries, they communicate what they’ve learned and suggest its implications for new business directions. Often they are creative in displaying information visually and making the patterns they find clear and compelling. They advise executives and product managers on the implications of the data for products, processes, and decisions.

Given the nascent state of their trade, it often falls to data scientists to fashion their own tools and even conduct academic-style research. Yahoo, one of the firms that employed a group of data scientists early on, was instrumental in developing Hadoop. Facebook’s data team created the language Hive for programming Hadoop projects. Many other data scientists, especially at data-driven companies such as Google, Amazon, Microsoft, Walmart, eBay, LinkedIn, and Twitter, have added to and refined the tool kit.

What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.

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So you wanna be a data scientist? A guide to 2015's hottest profession 12-27

So you wanna be a data scientist? 

A guide to 2015's hottest profession

Are you good at math? Like, really good at math? Do you also know Python and, oh yeah, have deep knowledge of a particular industry?

On the off chance that you possess this agglomeration of skills, you might have what it takes to be a data scientist. If so, these are good times. LinkedIn just voted "statistical analysis and data mining" the top skill that got people hired in 2014.

Glassdoor reports that the average salary for a data scientist is $118,709 versus $64,537 for a programmer. A McKinsey study predicts that by 2018, the U.S. could face a shortage of 140,000 to 190,000 "people with deep analytic skills" as well as 1.5 million "managers and analysts with the know-how to use the analysis of big data to make effective decisions."

The field is so hot right now that Roy Lowrance, the managing director of New York University's new Center for Data Science program says he thinks it has peaked. "It's probably in a bubble," he says. "Anything that gets hot like this can only cool off." Still, NYU is looking to expand its data science program from 40 students to 60 over the next few years. The current school year won't be over for another five months and 50% to 75% of its students already have firm job offers.

Why the explosion? Linda Burtch, managing director of Burtch Works, a Chicago-based executive recruiting firm, notes that while tech firms like Google, Amazon, Netflix and Uber have data science groups, the use of such professions is now starting to filter down to non-tech companies like Neiman Marcus, Walmart, Clorox and Gap. "All these are companies looking to hire data scientists," she says.

The hope is that such professional will unearth new information that will prompt new streams or revenue or let a company streamline its business. Pratt & Whitney, the aerospace manufacturer, now can predict with 97% accuracy when an aircraft engine will need to have maintenance, conceivably helping it run its operations much more efficiently, says Anjul Bhambhri, VP of Big Data at IBM.

Though IBM just released its freemium, cloud-based Watson Analytics program this month, most often data scientists have to create homegrown software programs to analyze unstructured data, which is one reason that programming skills are required.

Schooling

Lowrance says there are basically three skills that a data scientist needs to possess: math/statistics, computer literacy and knowledge of a particular business domain (like autos, for example.) NYU's program teaches those so that each area of expertise builds on the other. When you graduate, you're sort of a jack-of-all-trades for data crunching. "When working on data science projects in coursework they have to do all the jobs," he says.

Not everyone has to go through a college course to become a data scientist, though. A company called Metis, for instance, started offering a 12-week data science boot camp in September. The program, in New York, costs $14,000 and admission is highly competitive. Metis Cofounder Jason Moss says that about half the students come in with a Master's or PhD.

Just a couple of weeks after the first boot camp ended in early December, Moss said six of the class's 15 students had job offers.

"I don't think it's a replacement for college," Moss says of his program. "I think college is about more than the fastest path to getting a job. I also don't believe that you have to have gone to college to be successful as a data scientist," he says. "There's a personality type - innately curious, has grit, wants to figure things out — that does well."

Anmol Rajpurohit, an independent data scientist and consultant, says being a fast learner is most important attribute for this line of work. "Generic programming skills are a lot more important than being the expert of any particular programming language," he says. "Living in an age of rapid technology advancement, we see languages quickly becoming obsolete and new languages quickly getting popular. Thus, a fast learner will go a lot farther than an expert."

Lowrance says that he believes boot camps and online-based courses can be helpful for candidates strong in some skills, but weak on others. One virtue of NYU's program is that it teaches the skills sequentially so that they build on each other. "We give you everything you need in an order that makes sense," he says.

What data scientists do?

"On an average day, I manage a series of dashboards that tell our company about our business — what the users are doing," says Jon Greenberg, a data scientist at Playstudios, a gaming firm. Greenberg is a manager now, so he's programming less than he used to, but he still does his fair share. Usually, he pulls data out of Apache Hadoop storage and runs it through Revolution R, an analytics platform and comes up with some kind of visualization. "It may be how one segment of the population is interacting with a new feature," he explains.

Greenberg got a Master's degree in statistics six years ago. He expected to go into government work, but was surprised to see that data scientists were so in demand in the private sector. "It was definitely not as hot a field then," he says. Now, he says he gets about one call or email a day from a headhunter. "It's not me," he says. "They probably bother everyone else [with this expertise]."

For Greenberg, employability is a plus, but he loves the work itself. "I think it starts with, you have to have an analytical mind. You have to be curious," he says. "You have to be flexible and creative and think of a different way to solve problems." The only downside of the job, Greenberg says, is the time spent "cleaning" data — pruning it to remove irrelevant findings. "That part's not that exciting and you spend a lot of time doing it," he says.

Rajpurohit says he spends a lot of his energy cleaning data, but also researching. "A significant part of my time is spent on research, because I often come across absolutely new problems and thus, have to study the latest literature on research in that particular field or reach out to experts on those topics for advice," he says.

"Despite its name, data science requires a good mix of both art and science. The science part is obvious –- mathematics, programming, etc. The art part is equally important –- creativity, deep contextual understanding, etc. Both the parts put together make one a great problem solver."

That said, Rajpurohit acknowledges that 'working in Data Science is not even remotely as sexy or glamorous as it is being perceived these days. This field is definitely gaining significance (and seeing high pay offers) across organization, but there is a lot of not-so-exciting tasks that a data scientist needs to work on almost daily basis."

Is this the career for you?

If the idea of spending much of your day programming and analyzing dashboards for relevant information appeals to you, then you might have the makings of a computer scientist. If you're merely motivated by the salaries, though, you may have a tougher time. Consider: People who fall into this line of work often spend their spare time writing programs and analyzing data just to amuse themselves.

Adam Flugel, data science recruiter for Burtch Works, recalls a recent candidate, a PhD holder, who he placed at Electronic Arts this fall. "What really stood out was the work that he was doing for fun in his free time," Flugel says. "He was involved in the online multiplayer game World of Tanks and led a “clan”, basically a team of players. He created a utility to scrape data from the game server and then ran analytics on that data to evaluate his clan’s performance. He used this info to figure out how to adjust their strategy, what types of players he should recruit to improve the team, etc."

If you don't love data for its own sake, then you will find it hard to compete with such candidates. Burtch, however, says everyone should learn to love data, if only for the sake of their career. "Within 10 years, if you're not a data geek, you can forget about being in the C-suite," Burtch says.

But what about Steve Jobs, Bill Gates and other such visionaries who saw the big picture and didn't get bogged down in the minutiae of data science? "That was 30 years ago," says Burtch. "I'm talking about the next 10 years."

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‘Data Scientist’ Replaces ‘Social Media Scientist’ In LinkedIn’s 2014 Top Skills List 12-23

‘Data Scientist’ Replaces ‘Social Media Scientist’ In LinkedIn’s 2014 Top Skills List - 

What a difference one short year makes. In 2013, my position of ‘Social Media Scientist’ which focuses on ‘social media marketing’ for clients and publishers was the most viable job in the land. This year, not only did ‘Data Scientist’ take the top spot in Linkedin’s “Top 25 Job Skills” list, the social media marketing skill was wiped clean off the 2014 list entirely.

Social Media Marketing no longer needed?

Does that mean my position has suddenly become irrelevant? Not really! What it says to me is that data collection, mining analysis and Cloud management are now the number-one recruitment darlings.

While us social networking folks were sought after last year, in 2014, those that slice and dice data came up more often in the 330 million member profiles. They, plus those involved in middleware, integration software, storage systems management and information security are the jobs in greater demand today.

Data Tech Gets its Sea Legs

This makes sense based on how many companies decided this past year to store their data in the Cloud -- whether it be the Internet of Things, smart homes, wearable devices, or TV streaming. All of these professional fields just matriculated from geek to chic - from hobby to mainstream vocation - so much so that TV tracking experts at Nielsen decided to add online television viewing to its area of purview.

Netflix & Amazon Prime Get Big Boy Pants

Nielsen Media, the company which has conducted standard TV measurement ever since that technology first graced our living rooms back in the 1950’s - has just announced plans to measure subscription online video services such as Netflix and Amazon Prime.

While both services have long been mum on ratings for both acquired programs and original series, Nielsen now plans to tabulate the data for them using that which was collected for clients and studios to see how their acquired content is performing on both Netflix and Amazon.

Data Scientists now Sexy

Just a quick perusal of articles posted by the likes of Fortune, Harvard, Gigaom and Time Magazine gave more spotlight time to Data Scientists as well. 
‘Data Stars’ like Andrea Burbank of Pinterest, Patrick Poels of EventBrite, Silvanus Lee of Dropbox and Surabhi Gupta of Airbnb made the Fortune list, while former Netflix Senior Data Scientist Mohammad Sabah noted his position at the streaming video platform required the capture and analysis of an incredible amount of data  -- trying to figure out what consumers’ preferences were and what they wanted to view next. 
At the time, Sabah said, 75 percent of users selected movies based on the company’s recommendations, and based on those findings, Netflix has sought to make that number escalate even higher.

To that end, Netflix’s most-interesting use of data might be its attempts to actually analyze what’s going on in movies themselves. Sabah said it had already captured JPEGs and noted the exact time that credits start rolling, and it also took into account other characteristics. For instance, It could make a lot of sense out of variables, such as volume, colors and scenery that might give valuable signals about what viewers really like.

What this means for finding jobs in 2015?

"People call them (data scientists) unicorns" because the combination of skills required is so rare, said Jonathan Goldman, who ran LinkedIn Corp.'s team that in 2007 developed the "People You May Know" button -- which five years later drove more than half of the invitations on the professional-networking platform.

Employers according to Goldman says the ideal candidate must have more than traditional market-research skills: the ability to find patterns in millions of pieces of data streaming in from different sources, to infer from those patterns how customers behave and to write statistical models that pinpoint behavioral triggers.

Anyone with "data science" in his or her job title on a LinkedIn page is going to get "100 recruiter emails a day," said Josh Sullivan, who leads a 500-person data-science group at the consulting firm Booz Allen Hamilton Holding Corp.

So Says the Social Media Scientist. . .

So with this somewhat disturbing news that my field of play is not on the top of mind awareness for recruitment, I am content in the fact, I’ve secured a solid client base over the years.

On the other hand, ironically data shows that social media still does matter. 

Ninety-two percent of business owners today recognize the need to invest in social media as a necessary part of their business strategy, as it relates to engaging and marketing to their consumers; that’s an increase from 2013, when that number was 86%.

And as newer social networking platforms surface with ‘payment models' such as Tsu, Bitlanders, MyLifeB, BonzoMe and Bubblews - in my estimation that’s a whole new crop of networks that are going to need Social Media Scientists like myself to manage, market and promote for SMBs as well as larger brands. For more on this topic, see my previous post titled, “2015 Prediction For Top Digital Job: ‘Paid Social Specialist.’”  

That’s enough work to keep me busy for at least another year — and who knows when Linkedin’s “Top 25 Job Skills” list rolls around in 2015, my skill set might just be back in high demand, once again!

Happy job hunting and recruiting in 2015 -- you, Digital and 

Social Media Scientists!

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