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Big data is a valuable asset, provided that the collected data is given meaning and value.

It is the data scientist’s role to sort through and interpret this data so that it can be used to guide the company’s operations. As a reminder, big data refers to all the digital data collected by websites, apps, physical sensors, social media, and other sources. This can include text-based information as well as videos and images. Artificial intelligence relies on data to improve its performance.

Overview and Responsibilities of a Data Scientist

His work is invaluable to company management, as data provides guidance on both operational and strategic matters (HR, management, marketing, etc.).

For example, in the telecommunications sector, thanks to usage metrics and forecasts, the sales department is able to offer customized plans. Often confused with data analysts—who specialize in a specific category of data related to a business or strategic issue—data scientists have a broader perspective and play a more cross-functional role.

Business implications

Data scientists work across a wide range of topics and in diverse fields, including energy, telecommunications, media, music, fashion, banking, insurance, tourism, healthcare, transportation, finance, and agriculture…

Big data is sweeping through every industry, which has fully grasped its strategic and financial implications.

Ethical implications
The GDPR, the law governing data retention and protection, has caused quite a stir.

There are indeed many ethical issues related to data (data breaches, cybercrime, commercial incentives, environmental impacts, etc.).

Data scientists must, of course, be familiar with these issues and take them into account in their work. Data and AI legal experts and ethics officers provide valuable support for specific and sensitive cases.

Aivancity's programs incorporate all aspects of artificial intelligence and its challenges, whether technical, technological, commercial, ethical, or legal, into their teaching. These comprehensive, hybrid courses enable future engineers to acquire the highest level of knowledge and a broad range of expertise.

Key skills

A data scientist is an excellent statistician and a highly skilled analyst. They are proficient in at least one programming language (Python, Java, Perl, C/C++, etc.) and machine learning. Algorithms are right up their alley.

In data science, curiosity is not a bad thing: asking questions helps build expertise. Identifying the problems to be solved and the opportunities that data can offer are essential skills.

Patient and adaptable, the data scientist knows how to present their findings in a way that is accessible to everyone they work with. Staying abreast of technological developments is essential, and experimentation helps drive the evolution of practices in their field.

Trends and factors driving change

In small businesses, data scientists often take on additional roles, such as those of data miner and data analyst. They may also work for a service company, such as an IT services firm.

In a larger company, he works with the big data team. Becoming a Scrum Master—that is, a big data project manager—is one possible career path. Leading a team and a department is a natural next step in his career. Entrepreneurship as a consultant is another option.

The systematic automation of data collection and sorting processes is expected to save data scientists 80% of their time. This shift in their responsibilities should give them more room for creativity and strategic thinking.

With its enormous potential and significant challenges, big data is a sensitive resource. Only experts such as data scientists can handle it in a way that balances technological advancements with ethical considerations.