9 Key Considerations When Building a Global Data Science Team

What considerations should be made when building a global data science team?

To help business owners make a better informed decision about building a global data science team, we asked CEOs and business professionals to share their best insights. From building agile teams to adding accountability through analytics, there are several things to consider when building a global data science team.

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Here are nine key considerations for building a global data science team.

Turn to Executive Search Firms

When building a team of people with specific skill sets in a specialized field, consider turning to an executive search firm to build your team.

When recruiting for a global data science team, find a search firm that specializes in the functions and sectors that you’re looking for that can help fill your personnel gap. A search firm has the ability to deliver a slate of candidates with the right mix of knowledge needed for a specialized team.

Jon Schneider, Recruiterie

Build Agile Teams

When building a global data science team, you should consider building agile teams. While it is important to have people who are specialists in certain areas, having agile teams with generalists or people who can function in multiple areas of your operations can reduce bottlenecks. Fewer bottlenecks can increase your overall team productivity.

Debra Hildebrand, Hildebrand Solutions, LLC

Know the Team’s Purpose

I think there are two main considerations to be made when building a global data science team:

  1. Ask yourself, “What is the strategic purpose of the data?” What are you trying to achieve through this data? Is it going to be used as a part of an algorithm? Is it going to be used to develop a new technology? Is it going to be used for statistical purposes? Is it going to be sold for marketing purposes?
  2. Build a team that is most capable of collecting, analyzing, and leveraging the data based on its strategic purpose. If the data is going to be used to build and develop new technology, build a team that’s suited for that purpose. If the data is going to be used for marketing, build a team that will be able to meet that purpose.

Phillip Lew, C9 Staff

Bridge the Technical and Business Sides

Data translators are a newer role, but an addition that should be seriously considered when forming a global data science team. Data engineers and scientists are responsible for maintaining data and creating algorithms.

However, data translators bridge the gap between the technical side of a data science team and its business application by producing solid, actionable business insights from that data.

Guy Katabi, Lightkey

Establish Clear Goals

When building a global data science team, taking a step back to envision and establish a strategy is essential. Even the best talent can stumble if clear goals and structure are lacking.

Consider how centralized the team will be, how many members are needed, and what strategy will guide operations. Details like these form the foundation of a collaborative and like-minded team. Make goals clear to the entire data team to avoid silos and foster cohesion.

Claire Routh, Markitors

Add Accountability Through Analytics

The NewVantage Partners Big Data and AI 2020 Survey reports that 26.8% of the participants had no accountability for the business intelligence insights being delivered. This may be closely related to the fact that 72.1% of the firms in the survey struggle with appointing a data science team leader.

We can all agree that investing in business performance analytics is valuable. With these tools, you can get full, clear, and real-time insights into your running processes and team performance. Together with investing in such a solution, take an additional step by establishing an accountable data science team leader.

Spiros Skolarikis, Comidor

Create a Scalable Team

You will find data science teams to be unique to each organization depending on their needs. However, when building a high-performing team, scalability matters. Part of achieving this is by ensuring that teams are cross-functional and value-driven.

Michael Thompson, Lurn Agile

Consider Cultural Nuances

When building a global data science team, it is important to consider cultural differences and norms. Many managers make the mistake of assuming that data is a universal language, and skip team building.

However, while team members may be working most directly with data, they also need to cooperate with each other. When building a global team, work styles can differ from country to country, region to region, or even location to location. When working remotely, teammates have fewer chances to interact and build a natural rapport.

Yet communicating and delegating are still essential to the team’s success, even if each group member has assigned duties. Managers should take time to introduce teammates, clarify roles, and foster camaraderie, even when the nature of the work is highly independent.

Michael Alexis, TeamBuilding

Recruit a Leader and Members From Within

One of the crucial aspects of having a powerful global data science team in your company is to load-balance the machine learning (ML) models throughout the business. Therefore, leading a team to deliver the needed data insights is on the team leader. Also, start the process by diving right into your executive team.

Be sure to check if any of your in-house employees have ever done any certifications on ML before you recruit any external talent. List current employees who can be efficiently trained to work within the data science team.

Eden Cheng, WeInvoice

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