🏆The Big T in Data Science Teams

🏆The Big T in Data Science Teams

No matter of which business area to set up a team, there is a wide range of considerations. Typically, we hire people for their qualifications, and in general we are forced to fire them because of their personality. Within HR and project management, a lot of literature and recommendations exist in relation to how the management ensures a newly established team a good development process, clear roles and handling of challenges. And some make a distinction between different types of teams, for example High Performance Team has been a popular theme in recent years.

Is a data science team different from other teams?

Maybe not, but my experience is that one can benefit from thinking the letter t in team as “the big competence T”.

Vertically, it is important at the bottom of the T to have technical competencies to ensure that data is stored and updated efficiently and wisely, a little further up vertically in the T there is a need for ETL and Data Mining competencies. For both, it is important to have people with extensive knowledge of data structures and good interpretations of the data in the business systems / source systems. These vertically oriented people are the specialists of the team. The more near to the bottom of the T, the more focused on defined technical issues.

Horizontally, it is important at the top of the T to have analytical skills that can bring business understanding and strategic perspectives into the team agenda. This is the hard part where we translate data science into understandable information (e.g. datavizualisation and dashboards made with an eye for the non-data savvy reader) for the business decision and concrete actions. This is people who have strengths within modeling, statistical analysis and not least communicative skills. These horizontally oriented people are often the generalists of the team.

When I hire employees for my team I have in mind that the personalities should be able to challenge each other in a positive way. That is my learning from the traditional HR perspective when putting people together. That means for me that everyone in the team has a development-oriented mindset. Then I am focused on having team members who gives the team both vertically and horizontally skills and experience in relation to the great competence T. In my opinion this ensures that the data science team creates solutions that the organization can use – and vice versa that the organization better understands the importance of getting and having control of data.

In my team today, I have team members with very different educational background: One has a PhD in mathematics, another is mathematical physicist, there is an economist with strengths into finance, a political scientist with statistical specialization, and a biologist with communicative skills. Myself I am educated as a social science evaluator.

Also in the perspective of standardization in the description of roles in at Data Science Team I think the big T has a lot of relevance as a framework over the job titles and description of functions needed.

I think thinking in the “competence T” may help everyone in the hiring of members to a team, especially to a High Performance Data Science Team.

Best regards

Jacob Jensen

 

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