How to lead data science teams: The 3 D's of data science leadership

schedule Aug 8th 11:00 - 11:45 AM place Grand Ball Room 2 people 177 Interested

Despite the increasing number of data scientists who are asked to take on leadership roles as they grow in their careers, there are still few resources on how to lead data science teams successfully.

In this talk, I will argue that an effective data science leader has to wear three hats: Diplomat (understand the organization and their team and liaise between them), Diagnostician (figure out how what organizational needs can be met by their team and how), and Developer (grow their and their team's skills as well as the organization's understanding of data science to maximize the value their team can drive).

Throughout, I draw on my experience as a data science leader both at a political party (the Democratic Party of the United States of America) and at a fintech startup (Even.com).

Talk attendees will learn a framework for how to manage data scientists and lead a data science practice. In turn, attendees will be better prepared to tackle new or existing roles as data science leaders or be better able to identify promising candidates for these roles.

 
 

Outline/Structure of the Talk

  • Overview of my background as a data scientist and data science leader
  • Framework for how I think about what a manager or leader does
  • Outline and deep-dive on the 3 D's of data science leadership
  • Q&A

Learning Outcome

Participants will have learned a framework for how to manage data scientists and lead a data science practice as well as why and when this framework can be useful to them. Participants should leave the presentation better prepared to tackle new or existing roles as data science managers and leaders or better able to identify promising candidates for these roles.

Target Audience

data scientists looking to move into management roles, data science managers, data science executives

schedule Submitted 5 months ago

Public Feedback

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  • Kuldeep Jiwani
    By Kuldeep Jiwani  ~  4 months ago
    reply Reply

    Hi Juan,

    You have chosen an interesting topic and I am sure many people would like to hear about it.

    Too better understand the gist of your talk, can you highlight some of key industry pain points you wish to cover in the talk.

    Like some common management issues you are trying to address that are commonly seen in most of the organisations. Some areas of concern that are hindering the growth of Data Science in an organisation, etc.

    • Juan Manuel Contreras
      By Juan Manuel Contreras  ~  4 months ago
      reply Reply

      Hi Kuldeep,

      Thank you for your question! I would break down the key industry pain points that my talk will address into two groups: strategic (pain points faced by heads of data science) and managerial (pain points faced by data science managers).

      Strategic

      Strategically, many organizations do not fully understand how to make the most use out of a data science team. Here, the role of the head of data science is (1) understand what value the company expects from the data science team, (2) recognize what value the the company is missing in these expectations, and (3) identify the most effective ways of reducing the discrepancy between (1) and (2). 

      Reducing this discrepancy can involve solving these pain points:

      • Educating the organization about the right kind of data infrastructure to support an effective data science team (e.g., an analytics team for data analyses, data engineers for model deployment infrastructure)
      • Placing data scientists appropriately in the organization structure to enable them to drive impact and influence others (e.g., forward-deployed data scientists rather than a centralized data science team)
      • Empowering data scientists not only to be technical contributors, but also to be data product managers—thinking thoughtfully about how the work they do is a product that will be used by others

      Managerial

      From a managerial perspective, data science managers face many of the challenges faced by other managers. But there are some key differences in managing data scientists that effective data science leaders must be able to understand and work to address.

      • Data scientists, by virtue of their background as academics and as a result of the expectations business partners tend to have of them, can be pigeonholed as mere technical contributors. Data science managers must find effective ways of teaching data scientists to measure the impact of their work and to translate business requirements into technical work product that solves business problems most efficiently.
      • Data scientists are a diverse group with varied technical backgrounds. Data science managers must learn how to manage technical contributors when there is a technical gap between what the manager knows and what the data scientist knows. Relatedly, data science mangers must build a culture and work processes that work equally well for all data scientists, irrespective of their different backgrounds.

      Please let me know if this answers your questions! Happy to dive deeper into any of these points.

      • Kuldeep Jiwani
        By Kuldeep Jiwani  ~  4 months ago
        reply Reply

        Hi Juan,

        Thanks for the detailed and precise elaboration. It looks perfect :)


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