Creating a Data Science Team

Image of three workers at a desk

What it means to be a data science team  

Different Data Science Teams serve diversified roles across organizations and industries. This is all depending on the organization’s business objectives, resources, and analytics maturity.

Generally, a data science team is composed of Data Scientists, Data Engineers, Machine Learning Engineers, and Data Analysts; although individuals may wear many hats! 

Data Analyst: Generates business reports and data visualizations to help interpret business trends and activities. They provide tangible insights based on analytical results.

Data Scientist: Applies mathematics and algorithms against an organization’s data to create predictions and recommendations for action. This includes problem detection, transformations, and machine learning models.   

Data Engineer: Designs, implements, and tests the architecture of different data storage, loading, cleansing, and transformation pipelines for both business operations and analytics.

Machine Learning Engineer: Develops and optimizes the Machine Learning and Deep Learning model pipelines to continuously train, monitor, and test model performance at scale. 


What’s the difference between Data Analytics and Data Science?  

While many people use those two terms interchangeably, data analytics and data science are different from several perspectives. 

  • Data Analytics is a broad term used to describe the process of leveraging data to draw conclusions.
    Data Science is referring to more advanced analytical techniques, such as data mining and machine learning models. 
  • Data Analytics and Data Science projects usually require different duties and skillsets. Both use, cleanse, and maintain large data sets. Data Analysts usually generate reports, dashboards, and create data visualizations. Data Scientists focus more on developing machine learning models.
  • For companies that want to see immediate improvements, they usually conduct short-term data analysis projects to support their business strategies. For companies that want to gain competitive advantages using data, they are more willing to invest time and money into data science projects

What kinds of activities we do together to grow   

At Indellient, we understand the importance of nurturing the data science team and we have implemented several methods to help the team grow. 

Cross-functional team collaboration 

We encourage the data science team to collaborate with different teams within the organization, to build a data science culture throughout the company.

For example, the data science team has been working on an AI initiative with the DevOps team. Our goal is to build an application that helps the users to understand and optimize their cloud resource usage.  Sharing our respective expertise lets us explore exciting new paths in both domains. 

Data Science knowledge asset creation (white papers, blog posts, use cases)  

One of the key elements for team growth is to share team knowledge properly. As an IT professional service company, we have delivered many customized applications to our clients.  We discuss our project work and independent research to find broadly valuable experiences that should be shared with the team, and the broader community through blogs, whitepapers and conferences. By summarizing our success stories, we can learn the critical factors for data science development. 

Frequent discussion for the most trending data science research and techniques  

We also host bi-weekly data science discussion meetings within our company to share the latest data science research and techniques. During each session, one of the data science team members will pick a trending topic, collect information, and present the latest development regarding the chosen topic. Other team members will share their own thoughts about the topic. We continuously broaden our team knowledge through our discussions. 

How both we and our clients benefit from those interactions  

While we help the clients to implement data science applications within their organizations, we have also been learning from our clients. We have not only gained more domain knowledge about different industries and sectors; but also broadening and refining our production deployment practices. 

On the other hand, we continually look for ways our clients can improve their practices based on our experiences across many organizations. Additionally, we share our experience of data science team building and provide actionable recommendations for team growth. 

Conclusion 

Data Scientists are curious and always want to learn. It is truly important for the managers to nurture and maintain a creative and collaborative environment for the data science team. At Indellient, we try our best to make adequate expectations for data science development and help the data science team to stay business-focused while delivering tangible results with efficiency.  


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Indellient is an IT Professional Services Company that specializes in Data AnalyticsCloud Development ApplicationDevOps Services, and Business Process Management.

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About The Author

Hello, I am Meina Zhou. I am the Data Science Manager at Indellient. My core expertise lies in the application of proven data science tools and techniques to conduct business analytics and predictive modeling. I have used my business acumen and data science skills to solve business problems. I am a thought leader in the data science world and an active conference speaker. I enjoy public speaking and sharing innovative data science ideas with other people. I have received my Master of Science in Data Science from New York University and my Bachelor of Arts in Mathematics and Economics from Agnes Scott College.