A data janitor, the sexiest job of the 21st century

Daniel Lewis our consultant managing the role
Posting date: 7/17/2013 3:19 PM

A job invented in Silicon Valley is going mainstream as more industries try to gain an edge from big data.

The job description “data scientist” didn’t exist five years ago. No one advertised for an expert in data science, and you couldn’t go to school to specialize in the field. Today, companies are fighting to recruit these specialists, courses on how to become one are popping up at many universities, and the Harvard Business Review even proclaimed that data scientist is the “sexiest” job of the 21st century.

Data scientists take huge amounts of data and attempt to pull useful information out. The job combines statistics and programming to identify sometimes subtle factors that can have a big impact on a company’s bottom line, from whether a person will click on a certain type of ad to whether a new chemical will be toxic in the human body.

While Wall Street, Madison Avenue, and Detroit have always employed data jockeys to make sense of business statistics, the rise of this specialty reflects the massive expansion in the scope and variety of data now available in some industries, like those that collect data about customers on the Web. There’s more data than individual managers can wrap their minds around—too much of it, changing too fast, to be analyzed with traditional approaches.

As smartphones promise to become a new source of valuable data to retailers, for example, Walmart is competing to bring more data scientists on board and now advertises for dozens of open positions, including “Big Fast Data Engineer.” Sensors in factories and on industrial equipment are also delivering mountains of new data, leading General Electric to hire data scientists to analyze these feeds.

The term “data science” was coined in Silicon Valley in 2008 by two data analysts then working at LinkedIn and Facebook (see “What Facebook Knows”). Now many startups are basing their businesses on their ability to analyze large quantities of data—often from disparate sources. ZestFinance, for example, has a predictive model that uses hundreds of variables to determine whether a lender should offer high-risk credit. The underwriting risk it achieves is 40 percent lower than that borne by traditional lenders, says ZestFinance data scientist John Candido. “All data is credit data to us,” he says.

Data scientist has become a popular job title partly because it has helped pull together a growing number of haphazardly defined and overlapping job roles, says Jake Klamka, who runs a six-week fellowship to place PhDs from fields like math, astrophysics, and even neuroscience in such jobs. “We have anyone who works with a lot of data in their research,” Klamka says. “They need to know how to program, but they also have to have strong communications skills and curiosity.”

The best data scientists are defined as much by their creativity as by their code-writing prowess. The company Kaggle organizes contests where data scientists compete to find the best way to make sense of massive data sets (see “Startup Turns Data Crunching into a High-Stakes Sport”). Many of the top Kagglers (there are 88,000 registered on the site) come from fields like astrophysics or electrical engineering, says CEO Anthony Goldbloom. The top-ranked participant is an actuary in Singapore.

Universities are starting to respond to the job market’s needs. Stanford University plans to launch a data science master’s track in its statistics department, says department chair Guenther Walther. A dozen or so other programs have already been started at schools including Columbia University and the University of California, San Francisco. Cloudera, a company that sells software to process and organize large volumes of data, announced in April that it would work with seven universities to offer undergraduates professional training on how to work with “big data” technologies.

Cloudera’s education program director, Mark Morissey, says a skills shortage is looming and that “the market is not going to grow at the rate it currently wants to.” That has driven salaries up. In Silicon Valley, salaries for entry-level data scientists are around $110,000 to $120,000.

Others think the trend could create a new area of outsourcing. Shashi Godbole, a data scientist in Mumbai, India, who is ranked 20th on Kaggle’s scoreboard, recently completed a Kaggle-arranged hourly consulting gig, a new business the platform is getting into. He did work for a tiny health advocacy nonprofit located in Chicago and is now bidding on more jobs (he earns $200 per hour, and Kaggle collects $300 an hour). His Kaggle work is part time for now, but he says it’s possible that it could be his major source of income one day.

To the data scientists themselves, the job is certainly less sexy than it’s being made out to be. Josh Wills, a senior director of data science at Cloudera, says most of the time it involves cleaning up messy data—for example, by putting it in the right columns and sorting it.

“I’m a data janitor. That’s the sexiest job of the 21st century,” he says. “It’s very flattering, but it’s also a little baffling.”


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MeasureCamp Berlin

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In preparation for this year's MeasureCamp Berlin, we sat down with Benjamin Bock, communications lead, to discuss what to expect, as well as his thoughts on the industry in general. Here's what he had to say: Can you explain MeasureCamp for people who haven’t been yet? MeasureCamp is an open, free-to-attend analytics 'un-conference' made by analytics professionals for analytics professionals (and everyone who wants to get there) around the globe. In that sense, it’s different to any conference you know of. Our schedule is created on the day of the event, and our speakers are fellow attendees. Listen to talks, give a talk, and discuss topics that really tickle your fancy. What can we expect at MeasureCamp Berlin this year? Let’s begin with what you can’t and never will expect at MeasureCamp Berlin: Sales pitch presentations. We’ve all been there… you are visiting a fancy, expensive conference and all you get is Heads of 'This n’ That' talking about what their team did, what they spent money on and that you should buy Product X to be as Data-driven as them (mind the cynicism). At MeasureCamp you can expect talks and discussion rounds by around 150 fellow experts, who all know the daily adventures of cleaning Data, setting up analytics or debugging tracking code or running mind-bending analysis first hand.  What is your best tip for someone that has never been at MeasureCamp before? Don’t rush it! MeasureCamp is about mingling with the analytics community as much as it is about the talks and discussion rounds. Pick a few talks that really interest you and use the rest of the day to get to know other attendees. Our awesome sponsors are also more than happy to talk to you. What is the best advice you got last year at MeasureCamp? On a personal level, I was able to get some really good advice when it came to data privacy topics. GDPR was still fairly fresh and nobody really knew if what they had done was actually enough to not get into trouble. That’s the kind of advice you only get if you have the chance to talk to other professionals face to face. On another note, what are the most sought-after skills and technologies currently used? I can only speak of my experience here. On a hard skill level and depending on the individual role, you need a solid understanding of web technologies (JavaScript, HTML, CSS) and tag managing systems to be able to implement tracking (plus some knowledge in mobile development when your focus lies on apps). When it comes to analysing and visualising Data, you should understand the tool you are working with and its underlying Data-structures. Being able to retrieve tool-agnostic Data with SQL and running more sophisticated calculations (e.g. with Python) has become more and more important over the last few years. But there are some softer skills, that should not be overlooked as well. As an analytics professional, you should never assume that your knowledge and language are common ground. You need to be a strong communicator, who is able to explain complicated concepts broken down to the absolute basics. In your opinion, what will be the biggest challenge in digital analytics in the next year? Two weeks ago, I would have answered “bringing web and app Data together”. Now that we know Google is working on that topic, it’s still a challenge, but one I am happy to tackle in the coming year. Digital Analytics is constantly changing. What do you expect to be the most talked about topic at MeasureCamp this year? As a Tracking Specialist with a focus on Google products, I’d love to hear some talks about Google Tag Manager Custom Templates. But my top guess is, that the newly released Apps and Web properties beta for Google Analytics will be the talk of the hour. MeasureCamp Berlin is an open and free-to-attend 'un-conference', taking place this year on the 28th of September. The final batch of tickets will be released on the 21st of August at 03:00 PM (CEST). Click here for more information and to get hold of your place. 

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