Top Ways to Lead Data Science Teams in Organizations.
“Leadership is not about titles, positions, or flowcharts. It is about one life influencing another.” John C. Maxwell
Think about it.
Leadership is not an easy responsibility or a quality. And leading technical teams, especially the data science team is equally hard.
Reason: The way technical teams respond to leadership is different from how other teams respond.
Perplexing? Is it? Yes, it is to some extent.
While leading any team is challenging and individuals with leadership qualities are rare, leading data science teams come with its unique challenges. Challenges include but not restricted to –
- The expectation of stakeholders from data science teams is to deliver magic. It is difficult to make stakeholders understand that there is no magic it is all data – data that solves problems thus looks like magic.
- One of the major concerns of leading data science team is to first build the team. Reason: the challenge to find and recruit data science professionals is a tough nut to crack.
- To say data science professionals’ ethical predicament is baffling – is an understatement.
- There are no streamlined processes to manage data science teams and there is no agreed rule to manage the data science teams.
- There are few professionals who can don the leadership hat while being adept at tech as well.
- There are more such challenges that individuals leading tech teams face.
So, how does one go on to lead data science teams.
How To Lead Data Science Teams
Data science is one of the most sought-after careers in the present times. However, it is also one of the jobs, where the demand overflows the supply. In addition, to manage the talent that is hard to find, and recruit can be an arduous challenge.
The question is – how to overcome this challenge?
Well, there are ways one can lead data science teams. Mentioned here are some of the top ways.
Top Ways to Lead Data Science Teams
While there can be numerous ways to lead data science teams, here are some of the top ways that can help you lead data science teams.
- Begin with ‘Why’: It is important to know ‘why’ behind doing whatever you are doing. Reason: ‘Why’ drives action and data science are all about action. A good leader would dig deeper into the meaning and then come out with how and what. But they would first dive deep into the reason and then the
- Involve Shareholders: At any given point it is important to engage the stakeholders, because as a team you would need to convey significant to them. The effective leaders would:
- Recognize their shareholders who usually goes over and above the obvious individual requester
- Attend to the requests of stakeholders
- Recognize the needs of shareholders that could be different from the request.
- Implement Effective Processes: Effective processes doesn’t necessarily mean to implement a particular framework. It could be something simple like educating your team members about the importance of ‘why’ that should be the base of every good process.
- Create the Right Data Science Team: It is important to understand that like any other team a team of data science professionals should comprise right set of people. In case of data science team process as well, the composition of the team is dependent on various things like organizational culture and structure and along with the kind of problem the team is trying to solve. In addition, keep away from the misconception, which is data science teams need to have data science professionals. Truth is that a data science team should comprise all the professionals with all the roles in order to offer a solution. Implying you would need professionals like data architects, machine learning/AI engineers, business and data analysts along with data scientists as well.
- Foster a Culture that is Specifically Data Science: Fostering any culture starts from the top and in this case too, fostering a culture that is specific to data science should start from the top implying that top managers and other leaders should initiate in fostering a culture that is data science specific. In addition, it is important to get acquainted with your data science team members.
- Aim for Long Term Goal: While the job of data scientist is to create a machine learning model that is interesting, the focus, however, should be on creating models that are sustainable.
- Ethical projects and practices are important: Ethics are important. Ensure that your all data science projects, and data science practices are ethical. With data misuse and data breaches it is important to know what the ethical practices are related to data science teams.
While these are some of the ways to lead data science teams, it is important to know that technical people are not people manager. Also, another major challenge in leading a data science team is the skill shortage for various data science roles.
According to the industry experts, there is a shortage of data science professionals and not because there is lack of job opportunities, but because there are difficult to recruit. Reason: The supply is not able to match the demand in the jobs market, as the available professionals lack the required skills to perform data science jobs.
As per the experts, while small organizations can make do with software engineers, the large enterprises require skilled data science professionals – experts who are adept in different data science roles – to do the data science job.
Lack of skills is the obstacle why most of the data science positions go vacant year on year. So, what’s the solution. Upskilling is the solution. Yes, there are numerous ways you can upskill or reskill including but not restricted to –
- You can opt for Data Science online programs. There are numerous online programs available for data science professionals.
- There is no comparison to reading about new technology.
- You can also become a member of data science community. Once you become an active member of data science community you would be able to meet influencers and other data science experts.
- Remember data science is all about doing so, partake in open source projects. You could choose projects that may be centered around your interests and hobbies as a data scientist. GitHub is the platform where you would find numerous opportunities for projects that might revolve around your interest area. As a beginner you would gain an edge over your competitors when your portfolio displays projects from open-source platforms.
- Learn and master technical skills to become a successful data scientist. For any professional data scientists these are the top five tools, which you should master – Python; R; SQL; Tableau; Hadoop. For those just starting into data science field, it is important to have an in-depth understanding of these tools and languages.
- There are other non-technical skills as well that you would need to master to become a successful data scientist. Data science managers could also ensure that the team they plan to hire should have required skills for the project they wish to hire data scientist.
Another way to upskill, or reskill yourself is to get certification in data science. There are some of the best institutes that offer data science certifications for beginners, mid-level, and senior level professionals. In fact, being data science certified would help your career in ways you might not have imagined.