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Senior Machine Learning Architect
hace 1 mes
About the Role
We are seeking an experienced Senior Machine Learning Architect to join our Advanced Analytics Group at Bain & Company.
Key Responsibilities
- Collaborate with business consulting staff and leaders to develop data-driven solutions for clients across various sectors.
- Translate business objectives into data and analytics solutions, and communicate results to clients.
- Manage the development of reusable frameworks, models, and components.
- Drive best practices in machine learning engineering and MLOps.
- Develop relationships with external data and analytics vendors.
- Provide thought leadership in state-of-the-art machine learning techniques.
Requirements
- Advanced degree in a quantitative discipline such as Computer Science, Engineering, Physics, Statistics, Applied Mathematics, etc.
- 10+ years of software engineering, analytics development, or machine learning engineering experience.
- 3+ years of experience managing data scientists and ML engineers.
- Strong understanding of fundamental computer science concepts, software design best practices, software development lifecycle, and common machine learning design patterns.
- Solid understanding of foundational machine learning concepts and algorithms.
- Broad experience deploying production-grade machine learning solutions on-premise or in the cloud.
- Expert knowledge of Python programming and machine learning frameworks (Scikit-learn, TensorFlow, Keras, PyTorch, etc.).
- Experience implementing ML automation, MLOps, and associated tools (e.g. MLflow, Kubeflow).
- Experience working in accordance with DevSecOps principles, and familiarity with industry deployment best practices using CI/CD tools and infrastructure as code (e.g., Docker, Kubernetes, Terraform).
- Extensive experience in at least one cloud platform (e.g. AWS, GCP, Azure) and associated machine learning services, e.g. Amazon SageMaker, Azure ML, Databricks.
- Familiarity with Agile software development practices.
- Strong interpersonal and communication skills, including the ability to explain and discuss machine learning concepts with colleagues and clients.
- Ability to collaborate with people at all levels and with multi-office/region teams.
- Ability to work without supervision and juggle priorities to thrive in a fast-paced and ambiguous environment, while also collaborating as part of a team in complex situations.
