Regardless, they help bridge the worlds of data science and software development. Sometimes this role is known as a machine learning engineer. This role enables models to be deployed (i.e., use a model in production) and requires some expertise in data science, as well as knowledge of how to effectively develop software applications. Data Science Developerĭata Science Developers design, develop, and code large data (science) analytics applications to support scientific or enterprise/business processes. In conjunction with the Data Engineer, they manage and merge large amounts of data and their related sources. In other words, this role creates and manages relevant data models, data storage systems and processes workflows. Data Science Architectĭata science architects design and maintain the architecture of data science applications and facilities. This role also helps to ensure consistency of datasets (e.g., meaning of attributes across datasets). They design, develop, and code data-focused applications that capture data, as well as clean the data. Data Engineerĭata engineers make the appropriate data accessible and available for data science efforts. It’s key to understand the difference between data scientists and software engineers and to manage the data scientists in ways that don’t alienate them into a different role. If you lead a data science team, you need to understand that data scientists might get frustrated if they are managed like software engineers. In short, they apply the scientific discovery process, including hypothesis testing, to obtain actionable knowledge related to a scientific or business problem. They know the end-to-end process of data exploration and can present and communicate data insights and findings to a range of team members. Data Scientists find and interpret rich data sources, merge data sources, create visualizations, and use machine learning to build models that aid in creating actionable insight from the data.
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