8 Strategies For Data Scientist To Build And Manage A Productive Team

The data science industry has gained momentum over the past few years. Companies from different industries and domain are incorporating data science in their business process, creating a significantly high number of job opportunities for young professionals more than ever before. No doubt, data science has the superpowers to take a business to great heights, however, those powers won't be much of a use if your company doesn’t have a strong data science team with all the required skills. But the major challenge here is to build that ultimate data science team. Even if you have the top talent, but your strategies for team building and retaining isn’t effective, you might end up breeding a work environment that would not deliver value to the team as well as the company. In order to help data scientist with their team building strategies and increase the productivity level, we list down 8 techniques you can adopt while building the ultimate data science team.

Establish Yourself As A Leader

To build a team of top data scientist, there has to be a leader who would not only lead but also mentor. And to be leader, you have to have a lot of knowledge about the domain — you just cannot leave a stone unturned to gain as much knowledge as you can. So, when you are starting with building your data science team, make sure you establish yourself as a leader with all the qualities — in terms of knowledge as well as leadership.

Planning Is The Key

This is one of the most important things to keep in mind while you are scouting and looking to hire data scientists for your team. Make a robust plan of how you want to carry out the hiring process. One of the best ways to plan is to figure out the roles you want to fill in the team, and that can only be figured out when you have a blueprint of the project you are working on and the professionals you already have. You should never expect someone to master everything. Rather, we should look for the specific skill they possess. And data science is no exception, you just can’t hire data scientists; you have to hire data scientists with a strong hand on the skill you are looking for. So, pen down each and every single detail as this would help you in hiring the specific talent.

Build A Culture Of Open Communication

Nobody wants to work in an environment, where they have to struggle to get heard. And data science being one of the core and vast domains, professionals look for a work environment where they can openly share ideas and share their views — both in terms of work and the organisation. To create a culture like that, team supervisors or leaders need to communicate actively with all the members on a daily basis. Ask them what they are working on, how they are working, if they are facing any difficulty, and also, hold team meetings and brainstorming sessions to keep everyone motivated. This might sound very generic; however, it is one of the best team building strategies. Also, make sure that your data science team is updated with every single change happening in the firm. It would make them feel they are an important part of the organisation.

Set Goals For The Team

Many organisations don’t follow the concept of defining goals — they allow the team to work at their pace. However, that might be effective when you are core data science organisation. To make the team more productive, you have to set goals (such as weekly goals). And setting goals doesn’t mean putting pressure (this is huge misconception people have). When you set goals, you stay focused, you don’t get distracted by the things that don’t require attention. Furthermore, if the team is completely new, don’t set a high goal, rather, start with something that can be achieved with no struggle.

Appreciate Good Work

Paychecks are important, but appreciation matters too. Every employee who hustles day in and out feels more motivated by recognition and appreciation than by monetary rewards. Monetary benefits are always there in the data science domain, however, its tough to get recognized in this domain where competition is top-notch and is increasing rapidly. So, make sure to provide instant feedback — appreciate if it's good, and motivate if it's bad, but never demotivate. If you are having a culture where appreciation sits at the top, then you definitely going to have a team that would work hard and stay.

Be A Good Mentor

A team is not only defined by their work and the result they deliver, but also by the kind of leader they have. Being a supervisor or leader of a data science team, you are expected to have an immense amount of knowledge, and we have mentioned that in the very first point. So, when you are leading a team, make sure you are also using your skills and knowledge at actively. Help the team solve the complex problems — sit with them, work with them, accomplish with them. Also, mentor them as much as you can. It would not only make your team productive but will make you a better leader.

Be Supportive And Trustworthy

There is no surprise that many employees across the world don’t like their managers and don’t trust them. However, that shouldn’t be the case in a data science team. The data science domain itself is vast, complex, and confusing, so the professionals always seek support from their leaders. So, when you are leading a team of enthusiastic data science professionals, make sure you support them every way possible, making it sure that they know you value their contributions — try your best to get them the best projects, make sure they are pressurized by deadlines, etc.

Upskill The Team

Upskilling is one of the best ways to get rid of the hiring process. There are many organisations that choose upskilling rather than employing a new professional, and it is also beneficial in many ways — both for the employer and the employee. According to one of our studies, there is a significant shift in mindset when it comes to upskilling. Earlier the focus was on the upskilling of a few isolated people in a core data science team. However, today the organisations have started to realize that every employee must become data literate and “data smart”. Furthermore, MOOCs are gaining significant traction. According to another study of ours, 49% of the respondents extensively rely on MOOCs when it comes to upgrading their skills in new tech. So, when you feel your team is complete and is capable enough to take up other challenges, you can always go with upskilling. Hold training sessions where you teach your team other data science skills. It would not only help you get other projects done but also providing opportunities for the team to learn different skills that they didn’t know.