Data scientist

Tasks of the data scientist

• They collect data en masse and structure it for in-depth analysis.
• They spot irregularities and clean up the data to create predictive models.
• They evaluate and improve the performance of machine learning models.
• It generates dashboards and graphical representations of data to facilitate communication with the teams concerned.
• It draws up recommendations based on the data to guide the company's strategic decisions.

Data scientist skills

Technical skills

• Data scientists must be proficient in programming languages such as Python, R and SAS for data processing and analysis. They must be able to work with development environments such as Apache Hadoop and Spark. He/she is familiar with Map Reduce concepts for managing large quantities of data.
• In-depth knowledge of database management systems, particularly SQL and NoSQL, is required to organise, store and retrieve data efficiently.
• Statistical skills enable modelling and predictive analysis. Mastery of applied mathematics also helps to design and optimise efficient algorithms.
• Proficiency in web analysis tools such as Omniture and Google Analytics is important for analysing user behaviour on the internet and optimising marketing strategies. In addition, proficiency in data management tools enables data to be manipulated and prepared for analysis.

Soft skills

Project management and effective leadership skills enable data scientists to manage teams and bring their projects to a successful conclusion. Clear and persuasive communication enables them to explain technical concepts to their audience and convince decision-makers. Innovation and proactivity enable them to propose improvements and new approaches, while rigour and analytical skills ensure the accuracy and reliability of the data processed. Fluency in technical English is an asset.

Education and training for data scientists

Training to become a data scientist lasts 5 years after the baccalauréat, leading to a Master's degree or Master of Science (MSc) specialising in fields such as applied mathematics, business intelligence, data science, statistics, or even a specialisation in Big Data via an engineering degree. These programmes are offered by leading universities and engineering schools such as Centrale Supélec, École polytechnique, Télécom Paris and others.

In addition to engineering courses, business school courses specialising in marketing, Big Data management or statistics are also popular. MBA Big Data courses or masters specialising in data offer the advanced skills needed to manipulate and analyse large datasets.

It is common for graduates of these courses to initially work as data analysts, giving them practical experience of data analysis before progressing to data scientist roles.

Possible career paths

After a few years, they may progress to leadership roles such as Chief Data Scientist or Chief Data Officer. These positions involve managing a team of data scientists, supervising data analysis projects and being responsible for the analytical tools adopted by the company.

Another career path may lead the data scientist to become head of the company's information systems, where he or she would be responsible for overseeing all IT operations and data infrastructure.