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Data Science Team Structure [How to Build + Best Practices]

07th Sep, 2023
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    Data Science Team Structure [How to Build + Best Practices]

    Companies now need to take big data more seriously than just acknowledging it, and they must embrace data and analytics and integrate them into every aspect of their business. Naturally, this calls for assembling a competent group of data scientists to manage the organization's data and analytics. Because the profession is so young and many businesses are still attempting to figure out exactly what a competent data scientist should provide, picking the correct team members can be challenging. 

    Creating a strong data team structure can boost productivity and make projects more organized. Data science involves a team effort to identify patterns, predict outcomes, and reach conclusions from data sets. A crucial ability for a data scientist is knowing how to structure and run a data science team since it allows them to use their leadership abilities to motivate the team to perform better. You can enroll in a Data Science Bootcamp online to build analytical and programming skills to launch a career in data science. 

    Building a Data Science Team

    A data science org team is a collection of data scientists and other professionals who collaborate to analyze and interpret data. Businesses use data to comprehend trends and how they affect daily operations. Together, data scientists gather and examine the data to produce a concrete output, such as explaining graphs or patterns. They frequently possess exceptional analytical, mathematical, and analytical thinking abilities. They become a significant asset to a business where team members may collaborate to utilize their skill sets to the fullest. 

    So what is a Data Science course? A data science course provides training on the tools, technologies, and trends relevant to data science to help you understand the basic concepts of the domain. 

    Different Models for Structuring a Data Science Team

    If you are looking for different types of models or data science team names for one such group, then the list below is for you. 

    1. Decentralized Model

    In a decentralized team structure, smaller groups are formed for specialized tasks, and resources are assigned to each task individually. Due to the fact that it simply allocates data resources where they are required, this structure is frequently beneficial for businesses that just require minimal data analysis.

    Decentralized Model

    2. Centralized Model

    A centralized team structure consists of a single group of data scientists who handle all of the company's data science requirements. This data science team structure performs effectively for companies wishing to establish a data analysis division or that intend to integrate data analysis into every aspect of their operations. 

    Centralized Model

    3. Hybrid Model

    Decentralized and centralized team structures can be combined to create a hybrid team structure. The manager sees the data team as a unified entity under this organization, yet each individual works on tasks related to particular business operations or departments. 

    4. Democratic Model

    The democratic structure enables everyone in the company to access data through a portal and helps decentralize the data the team gathers. It enables every person in the organization to be a member of the data science team and increases transparency and interaction with executives and key stakeholders.

    Democratic Model

    5. Center of Excellence (CoE) Model

    According to the CoE model, a single center of excellence is responsible for managing data analytics across the organization. However, the data science team works independently in various business units or departments. As a result, the team's quality improvement standards and the firm's data science operations can grow.

    Center of Excellence (CoE) Model

    6. Federated Model

    This organizational team structure enables an analytics team to operate from the CoE while allocating data science specialists to particular projects or other business areas. This method incorporates the CoE model's decentralization with coordination for a larger and more effective team.

    Federated Model

    7. Consulting Model

    The team is organized into consultants under the consulting framework, who, when needed, provide their services to particular business needs or departments. As a result, businesses save money on centralized systems and can be more flexible with task requests.

    Consulting Model

    8. Functional Model

    In this data science team structure, the team is assigned to one functional department under the functional organization, where it distributes its resources and expertise. This organizational structure often applies to companies with simpler data analysis requirements, such as startups.

    Functional Model

    How to Structure a Data Science Team?

    Your data science team's efficiency can be increased, and you can build an accountability network with company executives and stakeholders by selecting a certain data science team structure. A team structure also aids in defining responsibilities for team members and distributes duties according to positions and skills. Consider the examples of the different common data science team structures given above before structuring your data science team.  

    Best Practices for Managing a Data Science Team

    Check out the best practices followed in the data science department structure. 

    1. Assign Specific Roles

    You can give duties to each person in the data analytics team after selecting a team structure depending on the demands of the firm. Your employees' main competencies are highlighted in specific jobs where they might be most useful, and each team member becomes accountable as a result. Further in this post, these diverse data science positions will be covered in more detail. 

    2. Engage with Stakeholders

    Interact with the stakeholders to improve communication between the data team and the stakeholders. It promotes trust and ensures that all sides of the operation share the objectives of the business. A firm's stakeholders frequently request details on initiatives, necessitating regular interaction between stakeholders, data science teams, and other specialists in business operations. When a project starts, inform stakeholders and give them a chance to ask questions during meetings, email threads, or phone conversations. 

    3. Assist Team Members in Honing Their Abilities

    The team may become more creative and produce better work if they develop their teamwork abilities. By providing professional mentoring or coaching from a leadership viewpoint, you may, as a manager, aid in your team's skill development. It entails putting an emphasis on enhancing strengths and addressing team weaknesses. You can concentrate on improving your team's time management and stress management skills. Along with increasing growth and creativity, supporting team members' skill development helps foster better trust between the company's management and workforce. 

    4. Make Your Workplace and Team Environment Positive

    A friendly and efficient work environment and team culture can boost your team's performance to the fullest extent. Managers can foster a healthy team culture by fostering principles like sincerity, integrity, timeliness, professionalism, and creativity. To set a positive example for your team, concentrate on demonstrating these principles throughout daily work and initiatives. To demonstrate your support for them and your willingness to make adjustments so that everyone is at ease and content, pay attention to the wants and problems of your team. 

    5. Enhance your Leadership Abilities in the Workplace

    One of the greatest approaches for a team leader to oversee a data science team is to concentrate on developing your leadership abilities professionally. You can enhance your leadership abilities by hiring mentors or business professionals as professional coaches. You can also enroll in leadership courses or seminars emphasizing these abilities. Pay special attention to enhancing your leadership abilities, such as communication, coaching, and listening. Having these abilities might make you more approachable and team-focused as a leader.  

    Data Science Team Roles 

    There is no such data science hierarchy maintained in a data science team, but a data scientist's team consists of the following job roles along with their responsibilities. 

    1. Data Scientist

    Finding and analyzing data sources, combining data sources, developing visualizations, and utilizing machine learning to build models that help derive practical insight from the data are all tasks performed by data scientists. They are familiar with the entire data discovery process and can present and communicate data insights and discoveries to other team members. They use hypothesis testing to acquire useful information about an issue. 

    2. Data Engineer

    Data engineers make the right data accessible and readily available for data science projects. They create, build, and code programs that are data-focused and collect clean data. Additionally, this function promotes the uniformity of datasets. 

    3. Data Science Architect

    The framework of data science infrastructure and applications is designed and maintained by data science architects. Thus, this position develops and oversees workflows, data storage systems, and related data models. They coordinate the management and fusion of massive amounts of data and its relevant sources with the Data Engineer. 

    4. Data Science Developer 

    Large data analytics programs are designed, created, and coded by data science developers to assist scientific or business/enterprise activities. This position implements models and calls for some data science expertise as well as practical software development knowledge. This position is sometimes referred to as a machine learning engineer. In any case, they support bridging the software development and data science sectors. 

    5. Data/Business Analyst

    Data/Business Analysts examine a wide range of data to obtain information about the operation of systems, services, or organizations and present it in a form that can be used or acted upon. They enable the data scientist to study a problem in a more effective way.  

    6. Process Master

    In order to help the organization achieve extraordinary results, the process master—also known as a Scrum Master—serves as a mentor, organizer, and obstacle breaker and helps everyone engage in understanding and embracing the project's goals, principles, and practices. 

    7. Subject Matter Expert

    Subject matter experts are persons who have an in-depth understanding of how to use analytics in a particular organizational environment. This position is responsible for making sure the necessary insights can be put to use. 

    Responsibilities of the Data Science Team

    A data scientist is a relatively new career path, so organizations hire them at various levels. Data scientist job title hierarchy can be of junior, mid or senior level. Here is a list of data science team responsibilities: 

    • Extraction of useful data from valuable data sources (Data Mining). 
    • Choosing features, building classifiers, and optimizing them using machine learning tools. 
    • Processing data, both structured and unstructured. 
    • Improving data-gathering processes to incorporate all pertinent data for building analytical systems. 
    • Preparing, cleaning, and ensuring the accuracy of data for analysis. 
    • Finding patterns and solutions by analyzing a lot of data. 
    • Creating machine learning algorithms and prediction systems. 
    • Reporting the results in a clear manner. 
    • Offer tactics and ways to deal with company difficulties. 
    • Link up with the business and IT departments. 


    Building a data science team might be challenging, given the field's rapid growth. But by heeding the suggestions in this article, you can assemble a prepared group to take on your data science tasks. The abilities and background of each team member should be taken into account while assembling a data science team. The team should have a diversity of subject expertise and a combination of technical and non-technical talents. The group must also be able to address complicated challenges together as a unit.  

    The KnowledgeHut's Data Science Bootcamp was created by industry experts to assist individuals in developing a successful career with practical knowledge and skills relevant to and required in data science. Create a resume that gets you placed and join an elite group of data scientists and engineers to advance your career through mentoring from experienced professionals. 

    Frequently Asked Questions (FAQs)

    1How do you structure a data science team?

    Your data science team's efficiency can be increased, and you can build an accountability network with company executives and stakeholders by selecting a certain data science team structure. The most common team structures are 

    • Decentralized  
    • Centralized  
    • Hybrid  
    2Who are the members of the data science team?

    Data scientists, Data engineers, Data Science architects, Data Science developers, Data/Business Analysts, Process Masters, and Subject Matter Experts are some of the members of the data science team. 

    3What are the 3 different roles in a modern data team?

    The 3 different roles in a modern data team: 

    • Data Engineer 
    • Data analyst  
    • Data scientist 
    4What is the highest position in data science?

    One of the most crucial positions in data science, in terms of pay, is that of a business intelligence analyst. Business intelligence analysts create strategic plans for organizations while guaranteeing that the necessary data is easily accessible. 


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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