Director, Machine Learning Engineering

Key Skills

Algorithms, Apache Cassandra, Apache Kafka, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Dask - Python library, Data science techniques, Databricks, DevOps, Docker, Google Cloud Platform (GCP), HDFS, Keras software library, Kubernetes-K8s, Machine learning techniques, Optimization, PyTorch, Scikit-learn, SPARK Programming, TensorFlow, Terraform

Job Description

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