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The hype of "big data" has created a mythical god called the data scientist: a lone-wolf, super-smart human with a solid foundation in computer science, modeling, statistics, analytics, math and strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

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The reality is that very few folks have mastered all those skill sets. As a result, data science is usually performed by teams, with people who have mastered one or some data science skills.

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Here is a partial listing of the job titles and skills on Rose Data Science Teams:
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Business architects: Team leaders. Strong business acumen and ability to communicate with senior business leaders and data scientists. Develops the architecture for information management and integrating data science and evidence based decision making. A change agent who has great persuasion skills to get the organization - at all levels - to use data science to make better decisions. They see the big picture, have business strategy talents and technology know-how. Business architects have a rare combination of business knowledge, process experience, transformation talents, methodology skills, and a winning personality that helps with communication and business change management.
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Data scientists: The top dogs in big data. Many of these folks have backgrounds in math or traditional statistics. Some have experience or degrees in machine learning, artificial intelligence, natural language processing or data management. Others are strong in the computer sciences with experience in high performance computing architectures, data mining and designing algorithms. Some are innovative modelers with strong business acumen.
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Data architects: Programmers who are good at working with messy data, disparate types of data, undefined data and lots of ambiguity. They may be people with traditional programming or business intelligence backgrounds, and they're often familiar with statistics. They need the creativity and persistence to be able to harness data in new ways to create new insights.
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Data visualizers: Technologists who translate analytics into information a business can use. They harness the data and put it in context, in layman's language, exploring what the data means and how it will impact the company. They need to be able to understand and communicate with all parts of the business, including C-level executives.
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Data change agents: People who drive changes in internal operations and processes based on data analytics. They may come from a Six Sigma background, but they also need the communication skills to translate jargon into terms others can understand.
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Data engineers/operators: The designers, builders and managers of the big data infrastructure. They develop the architecture that helps analyze and process data in the way the business needs it. And they make sure those systems are performing smoothly.
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In addition, data scientists are part of a bigger team in any organization. This includes business and IT leaders, middle management and front-line employees. The goal is to use data science to help the organization turn data into information - information into knowledge and insights - and valuable, actionable insights into better decision making and game changing strategies.
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In my opinion, the best data scientists do not have the strongest technical skills. Rather, they are team players: professionals who love to play with data, spot trends and learn truths few others know. The very best data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Most importantly, they have strong communication skills to help business leaders - and all members of an organization - apply data science and analytic results to critical business issues.
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I predict that data scientists who acquire business architecture skills - or business architects who master data science skills - will become the best business and political leaders of the future.  
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Tags: Data, Job, Science, Skills, Team, Titles, and

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