Director, Machine Learning Engineering

WHAT YOU’LL DOAs a member of the growing Data Science and Machine Learning (ML) Engineering team in Bain’s Advanced Analytics Group, you will:Develop, deploy and support industry-leading machine learning solutions, aimed at solving client problems across industry verticals and business functionsProvide thought championing in state-of-the-art machine-learning techniquesCollaborate closely with and influence business consulting staff and leaders as part of multi-disciplinary teams to assess opportunities and develop data-driven solutions for Bain clients across a variety of sectorsTranslate business objectives into data and analytics solutions and, translate results into business insights using appropriate data engineering and data science applicationsPartner closely with other engineering and product specialists at Bain to support development of innovative analytics solutions and productsTransform existing prototype code into optimized scalable, production-grade softwareManage the development of re-usable frameworks, models and componentsDrive best practices in machine learning engineering and MLOpsDevelop relationships with external data and analytics vendorsAct as Professional Development Advisor to a team of 3-5 machine learning engineersSupport AAG leadership in extending and growing our machine learning, engineering and analytics capabilitiesHelp develop Advanced Analytics intellectual property and identify areas of new opportunity for data science and analytics for Bain and its clientsTravel is required (30%)ABOUT YOUAdvanced 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 experience3+ years of experience managing data scientists and ML engineersStrong understanding of fundamental computer science concepts, software design best practices, software development lifecycle and common machine learning design patternsSolid understanding of foundational machine learning concepts and algorithmsBroad experience deploying production-grade machine learning solutions on-premise or in the cloudExpert knowledge of Python programming and machine learning frameworks (Scikit-learn, TensorFlow, Keras, PyTorch, etc.)Experience implementing ML automation, MLOps (scalable development to deployment of complex data science workflows) 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, DatabricksFamiliarity with Agile software development practicesStrong interpersonal and communication skills, including the ability to explain and discuss machine learning concepts with colleagues and clientsAbility to collaborate with people at all levels and with multi-office/region teamsAbility 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 situationsADDITIONAL SKILLSProficiency with core techniques of linear algebra (as relevant for implementation of ML models) and common optimization algorithmsExperience using distributed computing engines, e.g. Dask, Ray, SparkExperience using big data technologies and distributed computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr, Dask

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