What Requirements Can Make You a Perfect Data Science Leader?

Machine learning teams do not survive without skilled leaders. Pushing papers from one side of the desk to the other is what managing entails. The team is becoming more capable as a result of the leadership. Team productivity and project performance are held to a high standard. Leaders are in charge of developing relationships inside the team as well as with the rest of the company. The best teams do not necessarily have the best players. They have the most capable leaders. Mentoring and growth are essential elements in excellent leadership. Communication is essential. Leaders establish a team's identity and expectations. They take a group of people and put them together to form a cohesive unit that is more productive as a whole.

What doesn't work when it comes to leading a data science team?

Because we deny leaders their conventional sources of power, data scientists are difficult to lead. Add to that the difficulties of leading top achievers and the work's intricacy. Pop psychology and motivational posters are ineffective. Data scientists despise being in charge. Some have worked as consultants or in small groups. They've had a lot of autonomy and little to no supervision. Others have only worked with ineffectual leaders. They perceive the job as a roadblock to advancement. Some have worked under bosses that were dictatorial, dismissive, and mocked ideas that were not their own. In technical teams, competence is a frequent source of authority. Because the team naturally follows the greatest engineer, he or she is frequently elevated to a leadership role. This holds for data science teams as well. A tiny portion of the total responsibility is technical leadership. Even skill finally fails since someone more knowledgeable is always available. Machine learning is just too complicated for a single individual to be the most knowledgeable on every project. One of the most important aspects of leading a data science team is the leader's ability to surrender technical leadership without losing authority.

Two effective authority sources

Create a balance in which the data science team's actions benefit both the company and the data scientists. Leaders must take the time to learn about each data scientist's objectives and motivators, and then design a framework that compensates them for actions that provide value to the company. Because the data scientist is intrinsically motivated by those layered goals, alignment is a strong source of authority. The second aspect of alignment is linking team goals to individual goals. To avoid the dreaded "Not My Project" mentality, data science should be a collaborative effort. It's all too tempting for data scientists to get caught up in their initiatives at the expense of others. Setting up a structure that rewards everyone for team achievement encourages everyone to pitch in when they're required. Instead, then putting accountability on the leader, this paradigm promotes accountability for each other. This is how the second source of authority, trust, is established. The foundation for the team to be a part of the broader organization and for each data scientist to be a part of the team is built around aligned goals. When everyone sees how the team and the broader company are working together to achieve the same goals, they are more likely to trust each other. When they believe, whether or wrongly, that a person or organization is not completely dedicated to those aims, they will be just as determined to point it out and demand a solution. Only if the leader is willing to follow the team can he or she ask the team to follow them. Data scientists expect to be heard and their ideas to be implemented. As a result, a data science leader is frequently in charge of managing and communicating with senior management. A data science leader must be capable of resisting deadlines, projects, a lack of funds or personnel, and any other circumstance in which the team is ordered to undertake work that does not align with their understanding of the goals. This gap necessitates a leader who is capable of addressing the problem at whatever level is required.

Mentoring and team development

Leaders are in charge of increasing the team's capabilities. Their finest instruments for achieving that aim are hiring and mentoring. What kind of mentorship is required? Who should the team get on board? Is there a genuine need for fresh players on the team? People can develop throughout their lifetimes. Most people are discouraged from doing so, especially after they reach a senior position. It is self-evident that the discipline of machine learning requires ongoing education. Mentoring gives you the chance, structure, and direction you need to flourish. The two major paths for professional development are career path and capabilities. Data scientists become bored and seek a more difficult task. Talented people are attracted to companies that are growing.

Most data scientists aspire to increase their business value and their capacity to deliver concrete results. A leader begins mentoring by demonstrating their abilities. It's difficult to recognize your abilities. That candid self-evaluation typically needs the assistance of a mentor who can function as a mirror. What competencies does the company require in the next 6 to 18 months to accomplish the next cycle of projects? A leader gives orders in this manner. Show them what the company requires and let them select what interests them the most. Concentrate on enhancing their assets. A lot of data scientists want to be unicorns. Increasing a talent from beginner to expert is more beneficial than improving a skill from beginner to average.

The company, on the other hand, can only give so much opportunity. The interests and competence growth plans of a data scientist may not align with the demands of the company. Being a good mentor often leads to a bright data scientist leaving the firm, which puts a leader in a difficult situation. Leaders also mentor their successors as well as other mentors. The process of capability development is learned, apply, teach. Mentoring's long-term goal is to educate them on how to mentor others. Mentoring skills must be taught to senior team members. Leaders must recruit substitutes and train others to do their duties. If you are the only one capable of leading the team, you will not be promoted.

New data scientists are being hired

When a leader recognizes their potential to teach and provide a growth path for their team, the norms of recruiting are fundamentally altered. You don't need to recruit data scientists with a long list of qualifications. The team isn't looking for the ideal applicant. A developing, cohesive team understands that they can educate a recruit a lot of what they need to contribute. They have more faith in their abilities to get a recruit up to speed. Candidates with this mentality are more likely to be long-term contributors. Quick learners, ambition, solid intellectual underpinnings, and communication abilities are all sought by the team. You must also have a systematic hiring procedure as a leader. Inside and outside of the company, leaders' network. Internal promotions are the most effective way to fill new positions. Trying to screen someone during an interview is significantly less successful than building connections with possible recruits outside of the organization. A list of questions is provided for each stage of the interview process. Follow-up inquiries are permissible and can be more loosely framed. The procedure is well-defined, reproducible, and equitable. Each individual in the interview is given an equal amount of time to speak.

A leader needs a development plan and a mentor

You might spend so much time focused on the team's progress that you forget about your own. Mentors are essential for leaders. Leaders require a career path and a plan for personal development. From team to team, organization to business unit to business, leadership develops. Leading and developing data science teams for a significant portion of your career may be quite gratifying. You'll still need a mentor since your leadership style and effectiveness can always be improved. The professional path of leadership might lead to the establishment of your own company. A mentor can also assist you in this area. A good mentor will often help you get over them. They'll prepare you to be better than they are.