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
As a member of Bain’s Advanced Analytics Group, you’ll join a talented team of diverse and inclusive analytics professionals who are dedicated to solving complex challenges for our clients. We work closely with our generalist consultants and clients to develop data-driven strategies and innovative solutions. Our collaborative and supportive work environment fosters creativity and continuous learning, enabling us to consistently deliver exceptional results.
WHAT YOU’LL DO
Work with general consulting teams to understand ML aspects of business problems, and appropriately prioritize and execute Provide technical leadership for end-to-end technical solution delivery on client cases (from solution architecture to hands-on development work) Advise client executives on topics in ML engineering and roadmap design Develop statistical/ML models to be handed over to clients as prototype or production software Transform existing prototype code into scalable, production-grade software Write, test, deploy and maintain machine learning code across the full software development lifecycle Codify client work into repeatable software toolkits and solutions Regularly demonstrate code to other team members Peer-review code contributions by other team members Collaborate on (or lead) the development of re-usable common frameworks, model and components that can be highly leveraged to address common ML engineering problems across industries and business functions Drive best demonstrated practices in software engineering, and share learnings with team members in AAG about theoretical and technical developments in ML engineering Work with the team and other senior leaders to create a great working environment that attracts other great ML engineers Act as PD Advisor as needed Participate in recruiting and onboarding for other team members
ABOUT YOU
7+ years of engineering experience 1+ years of experience managing data scientists / machine learning engineers Shipped production, enterprise scale data products Expert knowledge of Python and SQL Proficiency in one or more of R, Java, C++, Scala, Go, Julia Strong track record of implementing statistical and machine learning models, deploying these, and maintaining them in production environments Strong understanding of fundamental computer science concepts, particularly data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance Solid understanding of foundational concepts and algorithms in statistics and machine learning, including linear/logistic regression, SVM, random forest, boosting, neural networks, dimensionality reduction, reinforcement learning, etc. Broad experience of machine learning frameworks and tools (e.g. Pandas, numpy, scikit-learn, TensorFlow, Pytorch, Keras, Huggingface) Understanding of probabilistic programming techniques and associated tools (e.g. Pyro, Stan, Tensorflow Probability, PyMC3), Bayesian inference and MCMC methods Experience using, designing and developing microservices and associated APIs, with a thorough understanding of REST, GraphQL, gRPC Understanding of data security and privacy regulations, key topics in cybersecurity, authentication and authorization mechanisms (including cloud IAM) Experience with 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 (Jenkins, Docker, Kubernetes, and Terraform, Containers, Git) Experience with cloud platforms (e.g. AWS, GCP, Azure, Databricks, etc) and associated machine learning products, e.g. Amazon SageMaker, Azure ML Experience in big data technologies, e.g. Hadoop, BigQuery, MapReduce, Apache Spark Experience working according to agile principles Strong interpersonal and communication skills, including the ability to explain and discuss technicalities of ML algorithms and techniques with colleagues and clients from other disciplines Ability to work independently and juggle priorities to thrive in a fast paced and ambiguous environment, while also collaborating as part of a team in complex situations