Director, Machine Learning Engineering - Boston, MA, United States - Karkidi

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
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