WHAT YOU’LL DO
* 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 the 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.
* Provide thought championing in state-of-the-art machine-learning techniques.
* Develop, deploy, and support industry-leading machine learning solutions aimed at solving client problems across industry verticals and business functions.
* 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%).
* Consideration will be given to individuals with a specialization in NLP or Computer Vision.
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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