How customer types shape data science roles and methodologiesWhen 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.Source: DALL·EOne of the most noticeable differences between B2C and B2B companies is the data volume and the unit of analysis.B2C: High Data Volume, Individual User Focus
Navigating Data Science: B2C vs. B2B Analytics | by Yu Dong | Jul, 2024
