To have successful projects, you need to build a productive and knowledgeable team, especially when you are a manager in charge of data science. Data scientists need to interpret patterns, offer predictions, and draw conclusions from the given data sets. They can help steer a company in a specific direction and help advise on where to go or what to action. That’s why it’s so essential to manage everything correctly.
What Do Data Science Teams Do?
Teams of data scientists take on a variety of tasks, including:
- Data set research
- Identification of trends
- Results comparisons
- Analysis for inefficiencies or errors
- Accuracy checks
- Creation of visuals of data for easier comprehension
- Explain data and its role/importance to executives
Data science teams will use data to help a company understand trends and their impacts on various business operations.
Best Practice for Managing a Data Science Team
Managing a data science team requires patience and careful planning. Here are some best practices to help you make the most out of your team and your projects.
Choose a Structure
There are various team structures that make sense for a data science team, depending on your goals. You’ll want to choose a format that helps you maximize productivity while creating accountability. Various structures will also make it easier to assign tasks (based on skills and position).
Structures to consider include:
- Decentralized. This structure allows for the creation of smaller groups for particular functions. Resources can be allocated accordingly. It’s often used with organizations that need simple data analysis as it can allocate resources only where it needs them.
- Centralized. This is where a single team addresses all of an organization’s data science needs. This works best for larger companies that want to create a data analysis department to use data science in all operations.
- Hybrid. This structure is a mix of centralized and decentralized structures. While the manager sees the team as one unit, each team member completes assignments according to specific business departments.
- Center of Excellence (CoE). CoE approaches create a center of excellence that oversees the analytics. However, the data science team acts in a decentralized manner. The goal is to assist in producing higher quality standards, allowing the company to grow data science operations in the future.
- Functional. Ideal for startups or smaller companies and more straightforward analysis, functional structures assign a data science team to a single functional department where the team focuses its resources and skills.
There are other structures to consider as well, including democratic (that increases transparency/communication between stakeholders and executives), federated (where a team works form a CoE but assigns experts to either specific tasks or other parts of the company entirely), or consulting (where a team is organized into consultants and allows them to offer their services to other departments or projects based on requests.
Once you’ve decided on how your team is organized, you’ll have a better idea of how to assign roles and manage tasks.
Assign Team Members Their Roles
Your data science team’s roles will be based on their fundamental skills. By assigning roles according to ability, you’ll be helping to create accountability for each team member.
Common roles include:
- Data Scientist. These team members use statistical methods/machine learning to interpret the data at hand. Typically, they are the team’s core and spend their time collecting, documenting, and analyzing the data.
- Data Architect. Like engineers, architects assist in designing data pipelines and will oversee them (along with other data collection tools). They work closely with scientists to provide the data and improve collection methods over time.
- Data Engineer. These team members build/test data pipelines intending to maximize efficiency.
- Data Analyst. These team members analyze the business needs of an organization and leverage the data to advise on changes to optimize a company.
- Data Translator. Data Translators translate the data, typically for business operations teams. They break down complex information into easy-to-understand content.
Teams may also require a Machine Learning Engineer who can create algorithms and models and effectively instruct the software to read the data and develop AI programs that can automate data-related processes.
Nurture Your Team Culture
As a manager, it’s essential to take time and understand what your team needs from you and their position in their company. The most significant element you can add to your data science team is to build an environment that is supportive and professional. While you can go a long way to building culture by giving a model your employees can mirror (by being professional, punctual, available, and embracing innovation and transparency), make sure you are actively listening to your team to see what’s working and what can be done better. By being willing to change an approach for the group’s betterment, you’ll be building a team that can meet the company’s needs and iterates on existing structures to improve processes for everyone continuously.
Encourage the Professional Growth of Yourself and Others
Technology and business are changing all the time. When you’re in charge of maximizing an entire company, you need a team that’s educated, agile, and consistent in its search for understanding via datasets.
To meet this challenge, it’s crucial to upgrade your teams’ knowledge base and skillsets constantly. The most successful data science teams and managers are always seeking avenues for professional growth and/or supporting the effort of others. Carve out space for education or time for developing valuable skills such as stress or time management. By encouraging growth, you’re building closer ties to your team and showcasing your comfort with them growing and succeeding in whatever direction personally interests them.
But don’t forget about yourself, either. Model the behavior you want to see in your team. That means taking time to professionally better yourself and inviting others to join in where they can.
Build a Good Relationship with Stakeholders
Another meaningful relationship for data science teams is the relationship between them and project stakeholders. Project managers need to be close to ensure the business’ (or project’s) goals remain aligned. Stakeholders will expect updates, which requires a project manager to be on top of the latest developments and to be able to effectively share them – sometimes to various parties. By staying communicative throughout the project (and not hiding from stakeholder questions or correspondence until a milestone or project is complete), you’ll be promoting an environment of transparency that can help guide current and future projects and assist the company in working more productively, making better decisions promptly.
Conclusion
The best thing a data science project manager can do is remain transparent and open to communication. This helps keep stakeholder buy-in strong. However, it also supports a team understanding their more significant role in a company or project and why their work matters. This ultimately leads to happier, more engaged team members. To maximize both the team and each individual’s satisfaction with their role, always encourage personal growth and make time for employees to take on individual learning opportunities to help bolster professional development.
A well-functioning and focused data team can significantly impact a business’ overall rate of success across many departments and factors.
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