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 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.
- 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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