Roles of a Data Science Team


There are many ways that a data science team can be structured. This may vary, depending on the size, scope, and goals of the organization. That said, the key roles within a team remain relatively consistent across organizations. Some of the primary roles that are often included on a data science team are:

  • Data Scientist: As the name might suggest, this is the core role of a data science team. It involves finding and analyzing data sources, combining data sources, developing data visualizations, and utilizing machine learning to build data models that help derive practical insight from the data. Required skills for a data scientists should include statistics, math, machine learning, programming, and communication.
  • Data Engineer: This engineer role in a data science team is responsible for making data accessible and available for the initiatives the rest of the team is undertaking. Data engineers design, develop, and code data-focused applications that capture data, as well as clean the data. Data engineers also frequently help to ensure the consistency of existing and new datasets and manage data pipelines across the MarTech stack and beyond. Data engineers should have strong skills in programming (in languages such as Python and others), databases, SQL, and data engineering tools.
  • Data Architect: This role designs and maintains the architecture of data science applications and facilities. Data science architects create and manage relevant data models, data storage systems and processes workflows. They also work with data engineers to manage and merge large amounts of data and their related sources12. Data science architects should have strong skills in software engineering, data modeling, cloud computing, and data science tools.
  • Data Science Developer: This role on a data science team designs, develops, and codes large data (science) analytics applications to support scientific or enterprise/business processes. Data science developers enable models to be deployed (i.e., use a model in production) and require some expertise in data science, as well as knowledge of how to effectively develop software applications. Data science developers should have strong skills in programming, software engineering, web development, and data science tools.
  • Data/Business Analyst: This role supports the data science team by providing domain knowledge, business context, and analytical skills. Data/business analysts help to define the business problem and the data science goal, as well as interpret and communicate the results of the data analysis to stakeholders. They also help to measure and evaluate the impact of the data science solutions. Data/business analysts should have strong skills in business acumen, statistics, SQL, Excel, and data visualization tools.

These are some of the primary roles on a data science team, but others may exist, depending on the specific needs and objectives of the organization. A few other potential roles include a Process Master (sometimes just referred to as a Project Manager) who oversees the data science workflow and project management, Statistician who focuses on applying rigorous statistical methods and testing hypotheses, as well as Subject Matter Experts who may provide domain-specific knowledge on particular topics of expertise.