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Data Scientist
hace 3 meses
Why SoftwareOne? SoftwareOne is a leading global software and cloud solutions provider that is redefining how companies build, buy and manage everything in the cloud.
By helping clients to migrate and modernize their workloads and applications – and in parallel, to navigate and optimize the resulting software and cloud changes – SoftwareOne unlocks the value of technology.
The company's 8,900 employees are driven to deliver a portfolio of 7,500 software brands with sales and delivery capabilities in 90 countries.
Headquartered in Switzerland, SoftwareOne is listed on the SIX Swiss Exchange under the ticker symbol SWON.Visit us at The role We are looking for a highly skilled Data Scientist with experience in MLOps to join our team.
The ideal candidate will have a solid understanding of data science and machine learning engineering principles, as well as hands-on experience in implementing and managing machine learning pipelines.
This role is crucial to optimize our model development and deployment processes, ensuring scalability and efficiency in our machine learning operations.
Responsibilities:
Develop, deploy and maintain machine learning models in a production environment. Design and manage machine learning pipelines using cloud tools and services. Collaborate with data engineers, developers and other data scientists to integrate models into existing applications and systems. Automate the process of training, validating, deploying and monitoring models. Ensure scalability, reproducibility and security of deployed models. Perform experiments to evaluate and improve model performance. Document processes and develop technical guides and manuals. #LI-LP1 What we need to see from you Proven experience in data science and MLOps roles. Experience with MLOps tools such as Kubeflow, MLflow, TensorFlow Extended (TFX), and similar. Ability to program in languages such as Python, and/or SQL. Experience in building and managing CI/CD pipelines for machine learning models. Familiarity with agile development methodologies. Excellent communication skills and ability to work in a team environment.
Desirable:
Strong knowledge in Google Cloud Platform (GCP), including services such as AI Platform, BigQuery, Cloud Storage, etc. Experience with other cloud platforms such as AWS or Azure. Knowledge in MLOps. Keep up to date with the latest technologies and practices in MLOps (Vertex and Azure ML). Ability to sporadically collaborate with the pre-sales team in the design and evaluation of some proposals. Job Function Software & Cloud Services
Proven experience in data science and MLOps roles. Experience with MLOps tools such as Kubeflow, MLflow, TensorFlow Extended (TFX), and similar. Ability to program in languages such as Python, and/or SQL. Experience in building and managing CI/CD pipelines for machine learning models. Familiarity with agile development methodologies. Excellent communication skills and ability to work in a team environment.
Desirable:
Strong knowledge in Google Cloud Platform (GCP), including services such as AI Platform, BigQuery, Cloud Storage, etc. Experience with other cloud platforms such as AWS or Azure. Knowledge in MLOps. Keep up to date with the latest technologies and practices in MLOps (Vertex and Azure ML). Ability to sporadically collaborate with the pre-sales team in the design and evaluation of some proposals.
We are looking for a highly skilled Data Scientist with experience in MLOps to join our team.
The ideal candidate will have a solid understanding of data science and machine learning engineering principles, as well as hands-on experience in implementing and managing machine learning pipelines.
This role is crucial to optimize our model development and deployment processes, ensuring scalability and efficiency in our machine learning operations.
Responsibilities:
Develop, deploy and maintain machine learning models in a production environment. Design and manage machine learning pipelines using cloud tools and services. Collaborate with data engineers, developers and other data scientists to integrate models into existing applications and systems. Automate the process of training, validating, deploying and monitoring models. Ensure scalability, reproducibility and security of deployed models. Perform experiments to evaluate and improve model performance. Document processes and develop technical guides and manuals. #LI-LP1
