Lead Data Scientist
3 weeks ago
Role : Lead Data Science with ML/Ops (DevOps)
Location : Minneapolis , MN ( Initial Remote / Hybrid )
Mandatory Skills :
- Design, build and maintain end-to-end (Data/Dev/ML) CI/CD pipeline
- DevOps tools such as Ansible, Docker, Kubernetes, Jenkins etc.
- MLOps tools such as ModelDB, MLFlow and Kubeflow.
- Azure
Responsibilities:
- Build, test and maintain the infrastructure and tools to facilitate the consistent and automated AI software solution development and release process.
- Design, build and maintain end-to-end (Data/Dev/ML) CI/CD pipeline that automates data collection, data analysis, experimentation, model training, model serving and monitoring in production. Achieve continuous delivery of AI software solution and updates to production at scale.
- Develop end-to-end (Data/Dev/ML) Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
- Collaborate with other data scientists, engineers, product teams and other key stakeholders.
- Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability
- Minimum +9 years in DevOps, Data Engineering and ML background. Familiar with best practices in the data engineering and MLOps community.
- Experience building and maintaining CI/CD pipelines. Demonstrated experience on a broad range of DevOps tools such as Ansible, Docker, Kubernetes, Jenkins etc.
- Experience building, deploying and maintaining ML models in production.
- Experience with MLOps tools such as ModelDB, MLFlow and Kubeflow.
- Familiarity with python tools for data science.
- Experience with cloud infrastructure. Experience with Microsoft Azure Platform is a plus.
- Excellent communication and teamwork skills.
- Ability to collaborate effectively with internal business stakeholders, outside partners and technology teams.
- Design, build and maintain end-to-end (Data/Dev/ML) CI/CD pipeline
- DevOps tools such as Ansible, Docker, Kubernetes, Jenkins etc.
- MLOps tools such as ModelDB, MLFlow and Kubeflow.
- Azure
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