In today’s world, data science has become a catch-all for much of the work done in business analytics, statistics, and data engineering. In addition, the problems data scientists face continue to grow in scope and complexity, making it ever more challenging to deliver business value with agility. As a result, companies increasingly expect a lot of their data scientists. However, the reality is that there are few people with both the deep modeling and analytics skills and the engineering expertise required to deliver advanced analytics in production. So how can companies solve for this?
David Schaaf explains how data science and data engineering can work together in cross-functional teams—with Jupyter notebooks at the center of collaboration and the analytic workflow—to more effectively and more quickly deliver results to decision makers.
David Schaaf is a director of data engineering at Capital One, where he leads data product development within the Financial Services Division. As part of his role, he guides agile teams to build data products for analyst and data communities with a primary focus on enabling self-service analytics, exploration, and insight discovery. David’s teams typically design data products using microservices, Angular, and Python and leverage core CI/CD practices for continuous delivery. David has more than 15 years of experience in software engineering and data analytics. He also has a wide breadth of knowledge across the financial services domain and in the retail industry. As a developer and analyst, David’s greatest interest is solving unique, complex problems and developing others as software and data engineers.
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