Director, Machine Learning Engineering

Found in: Jooble US O C2 - 2 weeks ago

Boston MA, United States Karkidi Full time

WHAT YOU’LL DO

As 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 functions
  • Provide thought championing in state-of-the-art machine-learning techniques
  • Collaborate 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 sectors
  • Translate business objectives into data and analytics solutions and, translate results into business insights using appropriate data engineering and data science applications
  • Partner closely with other engineering and product specialists at Bain to support development of innovative analytics solutions and products
  • Transform existing prototype code into optimized scalable, production-grade software
  • Manage the development of re-usable frameworks, models and components
  • Drive best practices in machine learning engineering and MLOps
  • Develop relationships with external data and analytics vendors
  • Act as Professional Development Advisor to a team of 3-5 machine learning engineers
  • Support AAG leadership in extending and growing our machine learning, engineering and analytics capabilities
  • Help develop Advanced Analytics intellectual property and identify areas of new opportunity for data science and analytics for Bain and its clients
  • Travel is required (30%)

ABOUT YOU

  • 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 (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, 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

ADDITIONAL SKILLS

  • Proficiency with core techniques of linear algebra (as relevant for implementation of ML models) and common optimization algorithms
  • Experience using distributed computing engines, e.g. Dask, Ray, Spark
  • Experience using big data technologies and distributed computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr, Dask
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