Lead, Machine Learning Engineer

Key Skills

Algorithms, Apache Hadoop, AWS, AWS Sagemaker, Azure, C++, Continuous Integration & Continuous Delivery - CI/CD, Cyber Security, Data science techniques, Databricks, DevSecOps, Docker, Effective communication skills, Git, Google BigQuery, Google Cloud Platform (GCP), GraphQL, HuggingFace, Java Programming, JavaScript, Julia, Keras software library, Kubeflow, Kubernetes-K8s, Machine learning techniques, MapReduce, MLflow, MLOps tools, NumPy, Pandas, Python Programming, PyTorch, React.js, REST API, Scikit-learn, SPARK Programming, SQL, TensorFlow, Vue.js

Job Description

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.

ABOUT YOU 

We are looking for someone who has:

• 3-5 years of engineering experience 
• Expert knowledge of Python and SQL 
• Proficiency in one or more of R, Java, C++, Scala, Go, Julia. Familiarity with JavaScript Frameworks e.g. React, Vue is a plus. 
• Track record of implementing statistical, machine learning and NLP 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. 
• Experience with 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 with containers, Docker and Git 
• 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) 
• 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