How to manage a team of Data Scientists

2nd April 2019


Happy teamGreat data science team management simply means understanding your team and creating the most suitable atmosphere for product delivery.

Managers who specialize in data teams, need to find a way to connect their work to the business and build that entrepreneurial spirit in each member.

There are several ways a data science team can find new value streams for a business. A good manager needs to be on standby to make this happen.

Make your team understand the business goal behind every project

The best way to get results from your team is to anchor their work to a larger organisational purpose. For every project your team works on, they need a vivid understanding of the goal behind it.

If possible, invite data science team members to product and strategy meetings. Hearing direct questions from the product owner or stakeholder will make the project objectives clearer – which is crucial to project execution.

Build trust with your team

Currently, there’s a little confusion as regards the roles of data scientists in organisations. Sometimes they are required to do the work of data engineers. This is where the team manager takes full managerial responsibility and regulates the demands made by the organisation.

Apart from making sure your team isn’t working on odd projects, you need to make sure they’re not burdened with unrealistic timelines. They just have to trust you enough to know you can handle loose ends like these.

Most importantly, don’t be too subtle with your requests and expectations. Dealing with a team of smart people will need a touch of strictness – as much as you go easy on them, don’t be afraid to speak straight from the shoulder.

Follow up on their everyday performance and focus on details.

Pay attention to your hiring process

This isn’t news but the importance of hiring the right talent for your team can never be overemphasized.

As expected, pay maximum attention to technical skills and experience. Keep in mind, looking out for other qualities in the candidate is important as well. A prospective team member should be curious and have a knack for learning new things.

The data field is always buzzing with new ideas and technology keeps opening up endless opportunities. A team of humble and listening team members will help make your managerial job easy.

Create learning opportunities

Data scientists can get easily bored if the projects aren’t challenging enough or if they feel stuck. Offer them learning opportunities and chances to explore their field – there’s always new research to look into or a hackathon to engage in.

Learn to specialise

For startups, specialisation might not exactly be the best way to get work done. There’s a need to have all hands on deck and allow an undefined flow of productivity.

This can also be applied to the operational process of a budding data science team.

It’s okay to have everyone on the teamwork do all kinds of data science. However, as the team becomes starts to break new grounds, specialisation will be required.

Your job, as a team manager, is to establish more defined roles and assign tasks accordingly.

By now, you understand the strength of each team member. Those who are full–stack data scientists and those who can stretch their skills through dedicated training.

Be the example

Being a manager requires utmost dedication and like it or not, you’re the first example to your team.

If you expect your team to prioritize and commit to deadlines, you need to do the same and do it even better.

Managing a team of data scientists will have you giving orders, setting deadlines, receiving feedback. It does not stop you from doing actual “science” work.

Understanding the nitty-gritty of every project helps you avoid pitfalls and put your team on the right path.

Build a data-driven work culture

Are all the decisions made in your team driven by intelligent data analysis? How do you measure your team’s progress per time?

As a data science team manager, you have to leverage data with your business objectives. To do that, your team needs to back up every discovery and decision with accurate data.

Data science can be a daunting discipline in some cases – processes fail and data sources sometimes prove difficult. You have a responsibility to offer consistent encouragement and remind your team members that creating value can take a long time.

Do your best to make sure projects are less overwhelming and be candid when necessary.

Read also: Can AI eliminate the hiring bias?

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Oluwatobi Ogunrinde

A passionate writer hoping to educate people with her work. Oluwatobi enjoys writing about entrepreneurship and work culture. When not writing, you will find her reading about international politics.

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