How customer types shape data science roles and methodologies
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Context
When considering a new company or a job offer, we often think about industry, company vision, growth opportunities, culture, etc. Today, I want to introduce another perspective: whether the business is B2B (Business-to-Business) or B2C (Business-to-Consumer). This distinction has a surprisingly large impact on data science roles.
Being a Data Scientist for over six years, I have spent half of my time working at a B2C company (Ancestry.com, a consumer genealogy company), and the second half at a B2B company (Brex, a spend management fintech company). Despite the distinct industries, I have noticed significant differences in data science methodologies and challenges brought by the different customer types. In this article, I will discuss the differences in data science analytics between B2B and B2C companies.
I. Data Volume and Analytics Unit
One of the most noticeable differences between B2C and B2B companies is the data volume and the unit of analysis.