Define the Data Science / Artificial Intelligence strategy, together with the Director Data & Analytics Development and Innovation and the Innovation Lead. The Data Science Manager is also responsible for implementing this strategy together with his/her team of Data Scientists. With the head of Data & Analytics Products and the D&A Portfolio Manager, the Data Science Manager defines the priorities and the (data science) capacity allocated to each product. The Data Science Manager defines with the Data & MLOps Engineers and the IT department the Data Science / Machine Learning technologies and methodologies that will ensure fast and robust development of Data Science / Machine Learning solutions.
Together with the Innovation Lead, drive data innovation within the company by leading a team of highly qualified Data Scientists (Senior - Medior - Junior) and ensuring their integration into product teams or data labs.
• Define a Data innovation strategy, in collaboration with the Innovation Lead and the Director Data & Analytics Development and Innovation • Define a framework for innovation that allows innovative ideas to be implemented
• Define which profiles need to be recruited/trained to develop data innovation in the company • Support / convince data product owners to use / apply data science in the development of their data products
- Setting up an innovation framework • Implementation of data innovations
Manage a team of highly qualified data scientists (senior - medior - junior) ensuring high retention of team members and delivery of efficient and robust algorithms. • Define the operating model of the team allowing the integration of data scientists in product teams and data labs while ensuring the robustness and efficiency of the algorithms developed thanks to a methodology and a common framework • Develop career and training paths for data scientists to offer them development perspectives • Manage the data scientists on a daily base • Manage the data scientist team capacity • Define objectives and assess team members • Manage relationships with external partners (consultants, freelancers, etc.) • Manage relationship with human resources for all subjects impacting data scientists -• Team stability • Algorithm delivery (feedback)
Create descriptive, predictive, or prescriptive statistical, machine learning or artificial intelligence models and combine these models to deliver data products that enable us to make better decisions, improve the customer experience and become better in its operations. The creation of models is the result of careful and exhaustive research of existing solutions either in a commercial environment or in the academic world. • Understand the Group's strategy • Evaluate different machine learning / artificial intelligence algorithms • Create complex to very complex machine learning / artificial intelligence algorithms • Perform combinations of several models • Popularize and present the models to the departments
Define and implement a data science methodology which all data scientists in the company will comply with and use. Link this methodology with the implementation of a Data Science / MLOps technological framework, including the Data Science and MLOps Framework allowing the production of the developed models. • Define and improve the methodology for each algorithm development within the team based on standard methodologies (such as CRISP DM) and our specificities. • Together with the Data & MLOps Engineers and the IT department, define and implement the right tech stack that will enable and support the Data Science / Machine Learning strategy• Together with the Data & MLOps Engineers and the IT department, define, implement and upgrade the data science / MLOps framework to ensure a fast and secure release of the developed models. • Ensure that data scientists follow the methodology and framework by making them aware of their importance. à• Implementation of the methodology and framework • Implementation of the right technologies (! Link with IT) • Respect of the methodology and framework by all team members
Be the main point of contact for the Data Science team for all strategic issues. • Work closely with the D&A Portfolio Manager to define capacity requirements for each data product through accurate estimation of workload and available capacity • Work closely with the Innovation Lead to define the capacity requirements for the data lab(s) through accurate estimation of the workload and available capacity • Work closely with Data & MLOps Engineers, Data Experts and IT Domain leads to define the strategies for Data Science and Artificial Intelligence technologies • Be the main point of contact for the Data Steering Committee (including directors, VPs and SVPs) for all Data Science and Artificial Intelligence matters • Be the primary reference person for all data science and artificial intelligence matters à• Feedback from the stakeholders
Drive Innovation with and within Data Science / Machine Learning
Proactively monitor relevant market developments in the following areas: - Advanced Analytics - Machine Learning - Statistics - Artificial Intelligence - Big Data - Cloud computing - Primary, secondary or third-party data collection technologies - Open Data nsure that all data scientists are aware of the latest market developments in these areas. Share this knowledge with colleagues within the organization, to keep abreast of the latest developments and help translate them into practice and participate in knowledge transfer within the organization. • Represen us externally during conference presentations • Participate in events, training • Maintain / build knowledge sources • Identify and meet your own training needs • Help data scientists define their training needs • Consult specialized literature and keep abreast of current events • Follow up on changes in its own area and communicate them to the relevant functions • Provoke and foster the exchange of knowledge and best practices with other Group companies • Trigger and promote the exchange of knowledge and best practices with other companies using their data massively • Provide training • Participate in conferences
Manage very complex analytical projects on your own or in collaboration with a (senior) data scientist. Translate business needs into clear deliverables, with clearly defined success criteria and defined and calculated KPIs. Apply 'Agile' principles in the development of solutions. • Contribute to projects based on one’s field of expertise. • Coach external consultants • Be responsible for the phasing and management of the project • Hand over the project to the client • Unite and coordinate the efforts of different parties (both internal and external) in a single project • Hand over the project to the client • Apply the SCRUM/AGILE method à Delivery of projects on time and with the desired level of quality
Define on an annual basis and manage the data science budgets, according to the requests and projects identified with the Portfolio Manager and the Innovation Lead • Define the Data Science budget on an annual basis according to the requests and projects identified with the Portfolio Manager and the Innovation Lead • Review and optimize the use of the budget during the various forecasting exercises • Manage daily the data science budget • ... Metric • Delivery of projects on time and with the desired level of quality
Requirements
• Academic o Master's degree (at least) in the following fields: mathematics, statistics, biostatistics, artificial intelligence, computer science, (experimental) psychology, civil engineering, business engineering with a data analytics / data science orientation, physics, ... • Experience o At least 10 years of relevant experience in data science, including 2 years with team responsibility • Skills o People management o Influencing skills o Analytical mindset o Visionary o Start-Up mindset o Communication o Popularization o Rigor o Pragmatism o Structure o Organization