5 Tips for Managing Data Science Teams

Data science has become increasingly important in business, especially with technological breakthroughs. Every company sees data as a business advantage and is willing to invest in data scientist manpower.

With businesses seeing data scientists as assets, the team must prove they can do their job properly. However, individual contributors can only recover with direction during working hours. That’s why it’s up to the leader to manage and organize the team to perform their jobs successfully.

I will share tips on managing data science teams well in this article based on my experience and others. So, let’s get into it.

1. Create a Collaborative and Inclusive Environment

 
In any working team in the business, the members would consist of individuals from diverse backgrounds. There are not only cultural differences but also skill differences, so it’s important to create an environment that accepts any kind of culture while still pushing for members to work together in collaborative ways.

It can be done by regularly sharing knowledge in team meetings, using a peer review process, or having cross-functional projects with several members with different skill sets and experience. What is important is eliminating any divisiveness and discrimination within the team.

Having created a collaborative and inclusive environment, the team would benefit from better problem-solving as the teams and more creativity in the working process.
 

2. Always Set Clear Goals and Expectations

 
Data science projects are often continuous and open-ended. If left without direction, team members would have difficulty knowing what to do and when to deliver their work, leading to disastrous results.

Establishing goals and expectations with teams regarding the data science project with the business goals could help the members focus and understand to work better. Try to create goals and expectations that are measurable and achievable with a proper timeline so we can understand the team’s performance.
 

3. Invest in Member Skill Development

 
As individuals, we know that we always want to grow. This has also proven true for our team members, who want to upskill themselves. This is especially true in the data science field, where the field evolves so fast, and people need to catch up to them so they can continuously bring value to the company.

By allowing member members to improve, we can keep our team’s skills up-to-date and maintain a competitive edge over the competitors. Try to allocate time for the team to learn new skills and tools while encouraging them to join workshops or seminars. You can also invest in a team to take certification or have a mentoring program from the senior to the junior.

Investing in skills is crucial if we want better retention for top performing members as it shows the company and the leader care for them.
 

4. Allow for Experimentation and Failure

 
I have seen that many junior data scientists cannot deliver their top potential because they are too afraid of failure or not meeting business expectations. While data scientists are hired to solve business problems, there are many times that it takes many iterations before we achieve this success. So, it’s much more intuitive for the team if we allow them to experiment in their work and could learn from the failure.

Data scientists work through experimentation, and we learn from all the failures that happen. By fostering an environment that allows members to try new approaches, we encourage them to become more creative and responsible with their work. Celebrate any kind of growth, as it would ultimately benefit the company.
 

5. Manage Work-Life Balance

 
Data science projects can be complex and could prevent the work-life balance. However, it’s important to have a healthy working arrangement suitable with the team members to maintain productivity while not burnout any of the team members.

Respect the personal time of your team members and encourage them to take breaks. It is important for them to enjoy life. Try to lead by example as well in maintaining a work-life balance.

Data science team members often overwork, so ensure they have a great work-life balance.
 

Conclusion

 
The data science team is a unique business unit with its own color. As a manager, we need to manage our team well. Here are some tips to do that, including:

1. Create a Collaborative and Inclusive Environment
2. Always Set Clear Goals and Expectations
3. Invest in Member Skill Development
4. Allow for Experimentation and Failure
5. Manage Work-Life Balance

I hope it helps!
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.