Data science is not sexy but diversity

Author: Xing Voong, posted on Feb 24, 2025

In Oct 2012, Harvard Business Review (HBR) published a well known article Data Scientist: The Sexiest Job of the 21st Century ¹. Ten years later, HBR updated their take on the topic in an article named Is Data Scientist Still the Sexiest Job of the 21st Century? ² As an insider, a data science graduate, and a practitioner, I want to say that data science is not as sexy as you think.

Data science may not be as sexy as you thought

In Oct 2024, I shared a music data set ³ that I had been working on for more than two months for a data science/ML project. The process of getting data alone was grueling. Not only was it technically challenging, it was also labor intensive. However, that is pretty normal for the data science and machine learning development life cycle, especially when you want to be more in charge of the data. After getting the data, you will spend more time on extract-transform-load or ETL. So far, you have not done any analytical work yet. If you work on a code project before, you would have known how stressful it is to find out that your code, after all, is … missing a semicolon. That being said, I am not sure what so “sexy” about that whole process.

Possibility for diversity

I don’t mean to discourage you, the readers, to not pursuing a career in Data Science. In fact, I want to encourage you more by giving a more realistic perspective. While data science is not as sexy as it sounds, it opens up the possibility for diversity in tech. For a while, tech was stereotyped as nerdy and mostly for men. Now it can still be nerdy, but a different type of nerdy, data nerdy. On the other hand, you need data experts for different fields, that is where domain experts in data come in. You can have a domain expert in software engineering, business, finance, sciences, and languages. People from different fields can leverage their expertise in data science, and AI/ML. The study of data science can lead to a career in software engineering, especially if you focus on software systems. More often, data science can lead to a career in analytics, data engineering, and data scientists. With a wide range of options, the field can invite a wide range of people which can lead to diversity. Diversity is not just in terms of faces, but also in terms of thoughts.

Don’t forget to choose an expertise

There are so many career options in data science. With so many options, it is easy to be distracted and become an expert in nothing. Therefore, don’t forget to choose an expertise and become great at it. For example, if you use data science kills for social science, keep yourself updated with social science knowledge and research. If you plan on becoming a software engineer on a data team, don’t forget to work on algorithm and system design. If you plan on becoming a data scientist, keep your statistics and probability sharp.

Prediction

I humbly think that soon data skills will become essential like reading, writing, searching information on the internet, or worse, scroll mindlessly on social media.