Facebook Inc. and LinkedIn Corp. are primarily known for their social networking prowess, but they’re also credited with helping to formalize an emerging job category known as data scientists -- and they both now employ a cadre of people in that role.
The data scientist position, which is also gaining ground at companies such as Google, Foursquare, Groupon and even financial services firm Zions Bancorporation, typically involves a combination of analytical, statistical, machine-learning, coding and quantitative skills, underscored by the harder-to-teach characteristics of curiosity and a willingness to explore large amounts of data.
“I used to call them data mining engineers or sometimes data miners or computational statisticians, but scientists seems apropos these days,” said Usama Fayyad, former chief data officer at Yahoo Inc. and current chairman and chief technology officer at ChoozOn Corp. “The complexity of data in volume, velocity and variety has increased enough to justify that.”
While the exact definition may vary from one organization to the next, the intimate relationship between data scientists, advanced analytics and “big data” doesn’t.
“Now that companies can reliably store massive quantities of data, the next step is what to do with the data you have,” said Monica Rogati, a senior data scientist at LinkedIn who has a doctorate in computer science from Carnegie Mellon University. “That’s where data scientists come in.”
The “vast quantities of data” Rogati is referring to means terabytes and beyond, though she wouldn’t put a number on the amount LinkedIn works with. But as Fayyad alluded to, volume is only one aspect of the multifaceted big data definition, as businesses are also facing the need to store and analyze a growing variety of data produced at an ever-quickening pace.
Tools for tapping into big data
In order to take advantage of the bigger and deeper pool of information, data scientists are tasked with wading through the noise to find the nuggets. In addition, organizations increasingly are interested in shining a brighter light on future behavior by using predictive analytics tools in an effort to uncover emerging trends and patterns. And with the larger data sets and new data sources that characterize big data, companies potentially can create better predictive models, which in turn can more accurately inform decisions on issues such as stocking store shelves and pinpointing fraudulent behavior.
Some examples of data-scientist want ads
Facebook: “Comfortable working as a software engineer and a quantitative researcher … a passion for identifying and answering questions that help us build the best products.”
Foursquare: “[T]alented individuals to help us with projects ranging across the full spectrum of machine learning and statistical programming disciplines … experience with prediction or recommender systems, search and ranking algorithms, and classification algorithms.”
Intuit: “Drive the creation of production software that leverages big data and advanced analytics in innovative ways.”
Groupon: “Are you excited about analyzing vast amounts of data, finding patterns in it and applying the insights to create business value?”
Generating some predictive insights from simple data sets or easy-to-understand information might require only basic data plotting skills, said Michael Driscoll, co-founder and chief technology officer at Metamarkets Group Inc., a San Francisco-based predictive analytics startup vendor that is focusing on the online media market. But as data becomes larger and more granular, Driscoll added, skilled data scientists can come in handy.Read full article at http://searchbusinessanalytics.techtarget.com/news/1280099515/Data-...