¿Es realmente la Ciencia de Datos el trabajo más sexy de este siglo? - Carlos Alarcón

What is the sexiest job of the 21st century?

You may be surprised to learn that one of the most in-demand and talked-about professions of the last decade is not only measured by its pay, but also by its complexity and impact on the global economy. In 2012, Harvard Business School described "Data Scientist" as the sexiest job of the 21st century. But why this term and what does it really imply? Let's unravel it.

Why is this profession considered "sexy"?

• Demand and high remuneration: The need for professionals with skills in statistics, mathematics, programming and business knowledge have turned data scientists into true "unicorns". It is a well-paying profession with demand that continues to rise.
• Rare and valuable skills: Being a data scientist requires a unique set of skills that are hard to find in the market, which adds particular appeal to the role.

How much has the data economy grown?

Since 2020, professions tied to data management have led job growth. According to the World Economic Forum, roles linked to data analytics, AI and Big Data are among the fastest growing. This reflects an increasingly data-centric economy.

What is the current situation in Latin America?

Regionally, particularly in countries such as Mexico, growth in data science professions has been remarkable, increasing 68% over the previous year. However, there is a significant challenge: only 18% of applications for these roles come from women. This points to the need to promote female participation in the field.

What are the new challenges and opportunities?

What changes have there been in the profession in recent years?

• Diversification of roles: Today, it is not only the data scientist who is sought after. There are several specialized roles such as data engineer, data architect and machine learning specialist. Each serves specific needs, expanding opportunities within the industry.
• Advanced tools: The emergence of AutoML and MLOps has facilitated automation in data modeling, shortening processes that previously required complex programming.

What are the environmental and ethical challenges?

• Environmental impact: Machine learning models require resources and generate a considerable impact in terms of CO2. Training advanced models involves an inordinate energy expenditure, similar to or even greater than the entire lifetime of a car.
• Ethical implications: Responsibility in the use of data cannot be underestimated. Incidents such as the Cambridge Analytica case demonstrate how misused data can influence massive decisions, such as elections. The community has begun to advocate for more ethical and responsible practices.

Where is the data economy headed?

The world continues to see a significant boom in the field of artificial intelligence, with amazing innovations in areas as diverse as imaging and text generation. This advancement creates new opportunities but also significant challenges, such as ensuring fairness and avoiding bias in algorithms.

We invite you to continue exploring this exciting area which, although it is debated whether it is still 'the sexiest', undoubtedly continues to be one of the most dynamic and influential. Are you ready to be part of this data revolution?