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Hiring Data Scientists: What to look for?

Prof. dr. Bart Baesens | July 4, 2014
Given the multidisciplinary nature of big data and analytics, a data scientist should possess a mix of skills: programming, quantitative modelling, communication and visualization, and creativity

A data scientist should excel in communication and visualization skills!

Like it or not, but analytics is a technical exercise.  At this moment, there is a huge gap between the analytical models and the business users.  To bridge this gap, communication and visualization facilities are key!  Hence, a data scientist should know how to represent analytical models and their accompanying statistics and reports in user-friendly ways using e.g. traffic light approaches, OLAP (on-line analytical processing) facilities, If-then business rules, ...  He/she should be capable of communicating the right amount of information without getting lost into complex (e.g. statistical) details which will inhibit a model's successful deployment.  By doing so, business users will better understand the characteristics and behavior in their (big) data which will improve their attitude towards and acceptance of the resulting analytical models.

A data scientist should be creative!

Finally, big data & analytics is a fast evolving field!  New problems, technologies and corresponding challenges pop up on an ongoing basis.  It is important that a data scientist keeps up with these new evolutions and technologies and has enough creativity to see how they can create new business opportunities.

Conclusion

In this short blog article, we provided a brief overview of characteristics to be looked for when hiring data scientists.  To summarize, given the multidisciplinary nature of big data and analytics, a data scientist should possess a mix of skills: programming, quantitative modelling, communication and visualization, and creativity!

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He has done extensive research on Big Data and Analytics.  His findings have been published in well-known international journals and presented at international top conferences.  He is also author of the books Credit Risk Management: Basic Concepts, and Analytics in a Big Data World published by Wiley in 2014.  His research is summarized at www.dataminingapps.comhttp://www.dataminingapps.com/.  He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics strategy.

 

 

 

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