Description & Requirements WHAT MAKES US A GREAT PLACE TO WORK
We are proud to be consistently recognized as one of the world's best places to work, a champion of diversity and a model of social responsibility. We are a Glassdoor Best Place to Work and we have maintained a spot in the top four since its founding in 2009. We believe that diversity, inclusion and collaboration are key to building extraordinary teams. We hire people with exceptional talents, abilities and potential, then create an environment where you can become the best version of yourself and thrive both professionally and personally.

WHO YOU’LL WORK WITH
Working alongside our generalist consultants, Bain's Advanced Analytics Group (AAG) helps clients across industries solve their biggest problems using our expertise in data science, customer insights, statistics, machine learning, data management, supply chain analytics and data engineering. Stationed in our global offices, AAG team members hold advanced degrees in computer science, engineering, AI, data science, physics, statistics, mathematics, and other quantitative disciplines, with backgrounds in a variety of fields including tech, data science, marketing analytics and academia.

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 sectorsTranslate business objectives into data and analytics solutions and, translate results into business insights using appropriate data engineering and data science applicationsPartner closely with other engineering and product specialists at Bain to support development of innovative analytics solutions and productsTransform existing prototype code into optimized scalable, production-grade softwareManage the development of re-usable frameworks, models and componentsDrive best practices in machine learning engineering and MLOpsDevelop relationships with external data and analytics vendorsAct as Professional Development Advisor to a team of 3-5 machine learning engineersSupport AAG leadership in extending and growing our machine learning, engineering and analytics capabilitiesHelp develop Advanced Analytics intellectual property and identify areas of new opportunity for data science and analytics for Bain and its clientsTravel 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 experience3+ years of experience managing data scientists and ML engineersStrong understanding of fundamental computer science concepts, software design best practices, software development lifecycle and common machine learning design patternsSolid understanding of foundational machine learning concepts and algorithmsBroad experience deploying production-grade machine learning solutions on-premise or in the cloudExpert 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, DatabricksFamiliarity with Agile software development practicesStrong interpersonal and communication skills, including the ability to explain and discuss machine learning concepts with colleagues and clientsAbility to collaborate with people at all levels and with multi-office/region teamsAbility 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 algorithmsExperience using distributed computing engines, e.g. Dask, Ray, SparkExperience using big data technologies and distributed computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr, Dask