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Meet the multiple personas of data scientists

This article was originally published on Technology.Info.
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Just when you think you have begun to understand the scope of work done by data scientists, along comes fresh research that reveals a more complex set of realities. "From the projected need for 69,000 additional big data specialists over the period 2012-2017, we know 60 per cent of businesses already struggling to hire people with data science skills" said Peter Robertshaw, Marketing Director at SAS UK and Ireland. "Such pressures mean some data scientists are taking on responsibilities unsuited to their personality types and skills, as the importance of big data analytics to organisations grows."

Personality typing for data scientists

The well-regarded system behind SAS’ personality typing is called DISC, and divides people based on the two distinct dimensions: extrovert/introvert and proactive/reactive. This creates four quarters: Dominance (proactive introverts), Influence (proactive extroverts), Steadiness (reactive extroverts) and Compliance (reactive introverts). Like any personality typing system, everyone shows aspects of all, and the ‘type’ is by no means fixed, but it’s one way of describing people.
Using this typology, SAS has identified six main types of data scientist:

  1. The Geeks, the largest group, 41% of the sample, have strong technical, logical and analytical skills. Their world is very ‘black and white’, and they’re often found defining systems requirements, and designing systems and processes.
  2. The Gurus are in interesting mix of reactive introversion and proactive extroversion, which means that they have strong technical skills, but also tend to be very persuasive. They are vital in organisations in bridging the gap between technical and ‘other’, persuading people of the merits of data analysis in decision making.
  3. The Drivers are the proactive introverts, and tend to end up as project managers and team leaders. They are very good at getting things done.
  4. The Crunchers tend to be reactive and value stability. They often gravitate towards highly technical support functions like data entry and preparation and statistical analysis.
  5. The Deliverers are similar to the Drivers, as they are very good at getting things done. However, they also seem to have a pre-disposition towards technical skills. This means that they’re best suited to developing and delivering detailed technical solutions.
  6. The Voices, the final major group, are similar to the Gurus, but with more enthusiasm and less solid technical expertise. Again, they’re often found ‘bridging’ between technical and non-technical, extolling the virtues of data science at a more conceptual and less technical level.

Of course there are several minor groups as well, including the Seekers, who tend to be found in research roles, the Ground Breakers, developing new approaches through a combination of inspired thought and general determination, and the Lynchpins, often in support roles.

The implications for working with data scientists

This sounds like fun, but it also has serious implications about the demands that are placed on data scientists. The survey found that around one quarter of data scientists, 26% of men and 27% of women, reported that they were adjusting their behaviours to meet job demands. In other words, their jobs required them to behave in ways that pulled them away from their personality type. This is likely to be an extremely stressful situation, especially if it continues for a long time. In a quite possibly not-unrelated finding, the survey suggested that about the same proportion of data scientists were highly stressed.
Some of this is perhaps inevitable. Data science is a fairly new discipline; the survey shows that most data scientists had been in the specialty for less than 10 years. And with that newness comes uncertainty about job demands. That’s both individuals not being certain about what they’re expected to do, and of course wanting to be flexible, and organisations not being certain about what they can or should demand from their data scientists.
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