Data sciences combine :

• mathematical specialties, including modelling data and decision-making objectives, exploratory statistics, learning and self-learning algorithms.
• computing specialties, including handling and structuring of data, manipulation of big data, visualization and processing of data.

The data scientists:

• are numerous because they are often self-appointed, and only take into account the expectations of the contractors,
• sometimes have at best only part of the spectrum of skills required,
• are often young professionals, with a constructive dynamism, but using unproven methods.

The companies:

• employ many data scientists, in teams that are too often in silos,
• and / or use many external consultants, which they cannot assess,
• must process their projects and progress in a methodical, reasoned and proven manner,
• ultimately need to objectively assess the quality and competence of data scientists,
• question their ability to capitalize on past achievements to optimize their internal processes and the allocation of resources.

• professionals with complementary backgrounds, from a broad spectrum and with experience of collaborating well with industry and services,
• university professors, research directors,
• network of international experts, including the ENS Paris-Saclay, the CNRS, the CEA, and their international collaborators.

• Appreciation of the skills and know-how necessary in relation to your objectives and challenges,
• Evaluation of existing teams, diagnosis
• Developing a target and plan of action