Over the past decade, we have seen explosive growth in the data science industry, with a rise in machine learning and ai use cases. Meanwhile, the title “Data Scientist” has evolved into different roles at different companies. Thinking about functions, there are Product Data Scientists, Marketing Data Scientists, those specialized in Finance, Risks and people supporting Operations, HR, etc.
Another common distinction is the tracks of DS Analytics (often referred to as DSA) and DS Machine Learning (DSML). As the name suggests, the former focuses on analyzing data to gain insights, while the latter trains and deploys more machine learning models. However, this does not mean that DSA positions do not involve machine learning projects. You can often find machine learning among the required skills in the job descriptions of DSA vacancies.
This overlap often causes confusion among aspiring data scientists. During coffee chats, I often hear questions like: Do DSA positions still require machine learning skills? Or do DSAs also implement machine learning models? Unfortunately, the answer is not a simple Yes or No. First, the lines between the two positions are always blurry (even a decade after data science work became a trend). Sometimes, within the same company, DSAs…