Senior Machine Learning Engineer

hace 4 semanas


Ciudad de México, Ciudad de México Bain & Company A tiempo completo
About the Role

We are seeking an experienced Senior Machine Learning Engineer to join our Advanced Analytics Group at Bain & Company. As a member of our team, you will work closely with our generalist consultants and clients to develop data-driven strategies and innovative solutions. Your expertise in machine learning will be instrumental in helping our clients solve complex business problems and drive growth.

Responsibilities
  • Work with general consulting teams to understand ML aspects of business problems and develop data-driven strategies
  • Provide technical leadership for end-to-end technical solution delivery on client cases
  • Advise client executives on topics in ML engineering and roadmap design
  • Develop statistical/ML models to be handed over to clients as prototype or production software
  • Codify client work into repeatable software toolkits and solutions
  • Collaborate on the development of re-usable common frameworks, model, and components that can be highly leveraged to address common ML engineering problems

About You

We are looking for a highly skilled and experienced machine learning engineer who can demonstrate expertise in Python and SQL, as well as proficiency in one or more of R, Java, C++, Scala, Go, Julia. You should have a strong track record of implementing statistical and machine learning models, deploying them, and maintaining them in production environments. Additionally, you should have a solid understanding of foundational concepts and algorithms in statistics and machine learning, including linear/logistic regression, SVM, random forest, boosting, neural networks, dimensionality reduction, reinforcement learning, etc. Experience using, designing, and developing microservices and associated APIs, with a thorough understanding of REST, GraphQL, gRPC, is also required. You should be able to work in accordance with DevSecOps principles and be familiar with industry deployment best practices using CI/CD tools and infrastructure as code.

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