I have been a data scientist since the early 2000s. After obtaining an undergraduate degree in geology at Cambridge University in England (2000), I completed Masters (2001) and PhD (2004) degrees in Astronomy at the University of Sussex, then moved to North America, completing postdoctoral positions in Astronomy at the University of Illinois at Urbana-Champaign (2004−9, joint with the National Center for Supercomputing Applications), and the Herzberg Institute of Astrophysics in Victoria, BC, Canada (2009−2013). I joined Skytree in 2012, and in 2017 the Skytree technology and team was acquired by Infosys. Machine learning has been part of my work since 2000, first applying it to large astronomical datasets, followed by wide ranges of application at Skytree, Infosys, Oracle, and then Dotscience.
My approach to data science is that the most important part of the process is always the business problem (or other context) and the understanding of what needs to be solved. Follow this with rigorous data understanding and preparation, and the analytics & machine learning is then enabled. Similarly, if production is the goal then this must considered as part of the process throughout, as must be clear and understandable presentation to all audiences that may have an interest in the project: business, IT, data science, product, engineering, and others.