Posts Tagged ‘Data Scientist’

Oxdata’s CEO on the Intersection of Big Data and Machine Learning

oxdataJosiah Motley recently wrote, “Big data is big money, and a relative new-comer to the game is trying to make a big impact. 0xdata (pronounced hexadata), started by SriSatish Ambati, is that new-comer. Their current flagship product, simply titled H20, is an open source platform used to crunch huge amounts of data to more accurately display analytic results. It is able to compute these large data sets by combining machine learning with advanced mathematical algorithms. H20 allows for customers to their entire data sets, instead of sample sets which are traditionally used for such processes. We recently had a chance to talk with SriSatish Ammbati, CEO and co-founder of 0xdata to help shed more light on their product.” Read more

The Most In-Demand Big Data Jobs Available Right Now

B5150336351_ae2a64336a_bernard Marr recently wrote, “It’s been estimated that by 2015, almost two million people will be employed in big data jobs in the US. Hal Varian, Google’s chief economist, is quoted as saying “…the sexy job in the next 10 years will be statisticians” and Tom Davenport, Distinguished Professor at Babson College, believes that a data scientist has the sexiest job of the 21st century. So what are these sexy jobs? Here’s a quick look at some of the positions available today that might allow you to break into the glamorous and exciting world of the big data professionals.” Read more

RoadMap Your Text Analytics Initiative

analyticspixWhat best practices should inform your company’s text analytics initiatives? Executive Lessons on Modern Text Analytics, a new white paper prepared by: Geoff Whiting, principal at GWhiting.com and Alesia Siuchykava, project director at Data Driven Business provides some insight. Contributors to the lessons shared in the report include Ramkumar Ravichandran, Director, Analytics, at Visa and Matthew P.T. Ruttley, Manager of Data Science at Mozilla Corp

One of the interesting points made in the paper is that text analytics can be applied to many use cases: customer satisfaction and management effectiveness, product design insights, and enhancing predictive data modeling as well as other data processes. But at the same time, a takeaway is that it is better for text analytics teams to follow a narrow path than to try to accommodate a wide-ranging deployment. “All big data initiatives, and especially initial text analytics, need a specific strategy,” the writers note, preferable focusing on “low-hanging fruit through simple business problems and use cases where text analytics can provide a small but fast ROI.

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Semantic Technology Jobs: Booz Allen Hamilton

BoozAllenBooz Allen Hamilton is searching for a Data Scientist in Rockville, MD. According to the post, this position will “Work with cross functional consulting teams within the data science and analytics team to design, develop, and execute solutions to derive business insights and solve clients’ operational and strategic problems. Support the development of data science and analytics solutions and product that improve existing processes and decision making. Build internal capabilities to better serve clients and demonstrate thought leadership in latest innovations in data science, big data, and advanced analytics. Contribute to business and market development for the government and commercial market in the health space.” Read more

Semantic Technology Jobs: Technorati Media

techmediaTechnorati Media is looking for a Data Scientist – Machine Learning in San Francisco, CA. According to the post, “We currently have an opportunity for a talented Data Scientist – Machine Learning to help take our business to the next level. You‘ll play a key role in the definition, design, and development of the Technorati Advertising and Data platform, which currently services hundreds of millions of requests per day. Design, implement and optimize algorithms for performance and revenue in a in highly distributed environment- a variety of advanced massively scalable technologies such as multi-threaded distributed systems, big data pipelines, machine learning, predictive algorithms and more. Work with large (terabytes of data, billions of daily transactions) structured and unstructured data sets.” Read more

GraphLab Create Aims To Be The Complete Package For Data Scientists

glabData scientists can add another tool to their toolset today: GraphLab has launched GraphLab Create 1.0, which bundles up everything starting from tools for data cleaning and engineering through to state-of-the-art machine learning and predictive analytics capabilities.

Think of it, company execs say, as the single platform that data scientists or engineers can leverage to unleash their creativity in building new data products, enabling them to write code at scale on their own laptops. The driving concept behind the solution, they say, is to make large-scale machine learning and predictive analytics easy enough that companies won’t have to hire huge teams of data scientists and engineers and build the big hardware infrastructures that lie behind many of today’s Big Data-intensive products. And, the data scientists and engineers that do use it won’t need to be experts at machine-learning algorithms – just experienced enough to write Python code.

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Why the Role of the Data Scientist is Growing

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Kevin Casey of Information Week recently wrote, “Old-school organizations will fuel the next swell of data-driven initiatives in IT. So what’s in store for the early movers and, specifically, their big-data professionals? How will the data scientist and similar roles evolve? ‘The role is becoming bigger,’ said Olly Downs, chief scientist at big-data analytics firm Globys, in a recent interview. By bigger, he means in every way — what was once a niche is now, at least in some companies, a driving force.” Read more

Machine Learning’s Future: Fortune 500 Buys In, Manufacturing Sees The Light

STServerMartin Hack, CEO and co-founder of machine learning company Skytree, has a prediction to make: “In the next three to five years we will see a machine learning system in every Fortune 500 company.” In fact, he says, it’s already happening, and not just among the high-tech companies in that ranking but also among the “bread and butter” enterprises.

“They know they need advanced analytics to get ahead in the game or stay competitive,” Hack says. For that, he says, they need machine learning algorithms for analyzing their Big Data sets, and they need to be able to deploy them quickly and easily — even if those who will be doing the deployments are coming from at best a background of basic analytics and business intelligence.

“There just aren’t enough data scientists to go around,” he says. It’s very tough to fill those roles in most companies, he says, “so like it or not, we have to make it much, much easier for people to digest and use this.”

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Semantic Technology Jobs: Teradata

teradata

Teradata is looking for a Data Scientist – Text Analytics in San Diego, CA. According to the post, “Teradata Aster is seeking experienced individuals with demonstrated capability in the applied analytic and/or data science space.  Proficiency in data manipulation, analytic algorithms, advanced math, and/or statistical modeling is required and application development experience a plus. We are looking for exceptional individuals to join our Professional Services team as an Analytic Data Scientists. This client-facing role will be engaged in the design and deployment of solutions. The key requirement is demonstrated capability in applied analytics, with MapReduce and database experience being preferred.” Read more

Semantic Web Jobs: Spartz

spartz

Spartz is searching for a Data Scientist in Chicago, IL. The post states, “We’re one of the fastest-growing companies in Chicago. The Spartz Network, including OMG Facts, GivesMeHope, and MuggleNet, attracts 17 million readers across a dozen websites, mobile sites, and apps. You’ll be responsible for: Helping evolve an advanced feature testing process for all new major and experimental features and products including observing the critical metrics central to our business. Analyzing existing data points & proactively finding your own data points to improve a product or overall product line. Fine-tuning product settings in order to optimize KPIs. Periodically providing site health diagnostics for each product.” Read more

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