It's definitely not Excel work. It's primarily using Python or R to build statistical models to do things like: - Predict the likelihood of a customer to churn

It's definitely not Excel work. It's primarily using Python or R to build statistical models to do things like: - Predict the likelihood of a customer to churn - Cluster customers into key segments for targeted messaging - Analyze and distribute customer support requests based on their content - Proactively identify bot or fraud activity - Create multi-touch attribution models for marketing touchpoints - Analyze multi-variate tests I think the problem you're hinting at is that many organizations say they're doing data science, or that they need data science, without knowing exactly what the role entails. As a result, many organizations hire "Data Scientists" that are doing the work of a Data Engineer, Analyst, and BI Developer all combined. I think 2015-2020 saw a huge surge in interest for Data Science, whereas 2020-2025 will have a surge in interest for Data Engineering.... mostly because organizations realized they can't do anything interesting with data until resilient, clean data sets are built for the organization.