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Webinar On-Demand: Your First Hire in Predictive Analytics (Hint: it is not a Data Scientist)


In a thriving analytic practice, the role of a data scientist is not defined by a person, but by a team. Within that team, several roles may be filled by one person-and several people may fulfill a given role.



Keith McCormick Your First Hire in Predictive Analytics
(Hint: It's Not a Data Scientist)


Speaker: Keith McCormick, Senior Consultant, The Modeling Agency

Download on demand (webinar given at TDWI on June 17, 2015).

Abstract

Many organizations are launching their first predictive analytics project and are not sure how or where to begin. With a great deal of hype around big data and data science, most companies understandably seek a data scientist with an extensive resume as their initial step.

The organization typically expects the new hire to stand up an analytic practice that will transform big data into smart data, and smart data into measurable impact. However, in a thriving analytic practice, the role of a data scientist is not defined by a person, but by a team. Within that team, several roles may be filled by one person-and several people may fulfill a given role. There are also roles that should not be fulfilled by the same person.

This Webinar presents the critical elements that need to be assessed, analyzed, and defined by an organization prior to forming a functional advanced analytics team. This preparation will enable organizations to properly align, right-size, and time the acquisition of analytic talent.

What You Will Learn

Organizations that are just forming a predictive analytics function-as well as those with existing staff-will view a framework to properly balance roles for all who contribute to, or benefit from, the end analytic product. Participants will learn how to:
  • Identify the primary roles that make up a functional analytics team
  • Determine when to staff a role and what to expect from each
  • Weigh the merits of internal versus external sourcing for each role
  • Appreciate how a data scientist should really function within the overall team
  • Avoid the high expense of over-hiring and the risks of under-staffing

 
Download on demand.