Empresa: Page Personnel
Provincia: Sevilla
Población:
Descripción:
Perfil buscado (Hombre/Mujer)
The successful candidate will join the company´s Technology Department as a Machine Learning Engineering Lead. In partnership with multiple stakeholders, you will focus on developing and delivering leading edge analytics solutions using Google Cloud and, as a key member of our engineering practice, you will mentor a small team of data scientists and analysts as we grow and drive the data science capability of the team. He/she will assume the following responsibilities:
• Define and support the research and analytical process to deliver business insights
• Responsible for advanced statistical and machine learning modeling
• Develop data driven analytical tools
• Machine learning – build models that can be used for asset health and grid operations
• Lead a small team of data scientists and data engineers
• Machine Learning Engineering Lead |International technology company that develops its own product.
Expectations for Skills and Experience:
• Extensive experience in GCP, preferably using Google BigQuery or similar data warehouses.
• Excellent programming experience with either SAS/SQL/R or Python.
• Extensive experience with advanced statistical and machine learning techniques.
• Ability to initiate and drive analytics projects from inception to delivery, and productionize and automate the process with excellent programming skills.
• Experience working with CI/CD pipelines.
• Experience with utility time series data will be a plus.
• Test automation experience.
• A good level of conversational English is essential.
International technology company that develops its own product.
We Offer:
• Full-remote and flexible hours.
• Technologically advanced project.
• Career plans.
Tecnologías: Machine Learning,GCP
Tipo de Contrato:
Indefinido
Salario: 60000 € – 90000 € Bruto/año
Experiencia: 3-5 años
Funciones: Big Data
Descubre más: https://www.tecnoempleo.com/machine-learning-engineering-lead-sevilla/machine-learning-gcp/rf-4e051a73927cb3211d48
