Data Science Team RolesData science is a broad discipline, spanning analysis, design, development, business, and research. The roles of Agile Data Science team members, defined in a spectrum from customer to operations.
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These roles can be defined as follows:
• Customers use your product, click your buttons and links, or ignore you completely. Your job is to create value for them repeatedly. Their interest determines the success of your product.
• Business Development signs early customers, either firsthand or through the creation of landing pages and promotion, and delivers traction in the market with the product.
• Marketers talk to customers to determine which markets to pursue. They determine the starting perspective from which an Agile Data Science product begins.
• Product managers take in the perspectives of each role, synthesizing them to build consensus about the vision and direction of the product.
User experience designers are responsible for fitting the design around the data to match the perspective of the customer. This role is critical, as the output of statistical models can be difficult to interpret by “normal” users who have no concept of the semantics of the model’s output (i.e., how can something be 75% true?).
• Interaction designers design interactions around data models so users find their value.
• Web developers create web applications that deliver data to a web browser.
• Engineers build the systems that deliver data to applications.
• Data scientists explore and transform data in novel ways to create and publish new features and combine data from diverse sources to create new value. They make visualizations with researchers, engineers, web developers, and designers, exposing raw, intermediate, and refined data early and often.
• Applied researchers solve the heavy problems that data scientists uncover and that stand in the way of delivering value. These problems take intense focus and time and require novel methods from statistics and machine learning.
• Platform or data engineers solve problems in the distributed infrastructure that enable Agile Data Science at scale to proceed without undue pain. Platform engineers handle work tickets for immediate blocking bugs and implement long-term plans and projects to maintain and improve usability for researchers, data scientists, and engineers.
• Quality assurance engineers automate testing of predictive systems from end to end to ensure accurate and reliable predictions are made.
• Operations/DevOps engineers ensure smooth setup and operation of production data infrastructure. They automate deployment and take pages when things go wrong
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