#WeAreIn for jobs that impact everyone’s life Your steady execution, creative problem-solving, and commitment to quality create a foundation that allows innovation to happen and success to grow. As a Data Scientist for Process Mining on our
Fidelidade - Companhia de Seguros, S.A. Para que a vida não pare A Fidelidade é a seguradora líder de mercado, vida e não vida em Portugal. Desde 1808 que a Fidelidade protege o futuro das famílias,
Responsible for performing data research and analysis to support business operations including: •Creating data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in large data sets •Blending historical data from available external and internal
Responsible for performing data research and analysis to support business operations including: •Creating data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in large data sets •Blending historical data from available external and internal
Ihre Rolle in unserem Team Als Data Scientist (m/w/d) treiben Sie die datengetriebene Entscheidungsfindung in unserem Unternehmen voran. Sie konzipieren und entwickeln neue Datenprodukte für unseren Servicebereich, insbesondere für ein gruppenweites Reporting. Perspektivisch unterstützen Sie zudem
Your role in our team As a Data Scientist (m/f/d), you will drive data-driven decision-making across our organization. You will design and develop new data products for our service business, with a particular focus on company-wide
Job Description: We are now looking for a Recruitment Business Partner to join our Airbus GBS team. In this role the main tasks and accountabilities you can expect may include: Analyse, understand and challenge the managers
Responsible for performing data research and analysis to support business operations including: •Creating data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in large data sets •Blending historical data from available external and internal