Читать книгу Minding the Machines - Jeremy Adamson - Страница 8

Those who transition to data and analytics mid-career have a key differentiator from those who have entered the field directly from university—breadth and context. The ability to leverage your multidisciplinary background from engineering, finance, sciences, and so on is valuable both to your career and the organization you join.

Though it would be almost impossible to compete with trained data scientists on a technical basis, it is the disciplinary and sector diversity of the team that drives innovation, and those who have worked in multiple industries and functional areas bring a unique perspective to the teams with whom they work. Rather than starting a new career as a new hire and individual contributor, with personal study and intentional self-reflection mid-career transitioners are often able to seamlessly make a lateral move. Having familiarity with the different AutoML and analytics-as-a-service offerings, combined with transferable managerial skills, can make for a powerful combination.

For the career transitioner, I hope that this book helps you to prepare for lateral movement into an analytics role and to use your transferable skills to add value in your new function.