Principal Data Scientist

Job Description

Principal Data Scientist

• The data mtier vision is defined and shared
• Complex and strategic data topics/questions are addressed and create more value.
• Evolutions of the mtier (technologies, norms, competencies ) are anticipated and knowledge passed throughout the company (training, network management and influence).

• Deliver high-level expertise in the necessary fields and help to solve complex or strategic problems more quickly and appropriately.
• Manage and transmit knowledge and know-how.
• Anticipate the impact of external changes (technology, standards, social behaviors, competencies needs, etc.) on the Company.
• Innovate and/or identify new scopes by implementing existing technologies on the market or through methodological approaches (marketing, supply chain, manufacturing, RDI, etc.).
• Be the ad-hoc contact person for specialized external partners or service providers to propose relevant analyses and create value for the Company.

• Provides a vision and/or scenarios in the field or elsewhere to influence the company's ambitions by ensuring that the top management understands the idea, technology, concept, tool, recognizes the benefit, and puts it in perspective in relation to the business.
• Positions his/her field's areas of focus in relation to those in other fields/mtiers to bring them into line with the company's areas of focus.
• Build complex or strategic algorythms including predictive models
• Advises new technologies (statistics, text mining, behavioural, geolocation, etc.) for extracting and analysing information, to obtain more value from datasets (Big Data)
• Builds mathematics, statistics or specialized software tools to extract and analyze data and designs datamarts and even data warehouses (data warehouses).
• Guarantees the development of skills in extracting, modeling, analyzing and visualizing data
• Evolves target information systems to increase data quality and richness
• Promotes new models or calculation methods and anticipates pros and cons according to a specific environment.

• Benchmarks other companies and stays aware of the latest trends and new technologies around big data
• Ensures the sharing of good practices (internally and externally)