Expert Manager, Machine Learning Engineer

Key Skills

Algorithms, Apache Hadoop, AWS, AWS Sagemaker, Azure, Big Data Technology, C++, Containerization, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Data structuring, Databricks, DevSecOps, Docker, Git, Google BigQuery, Google Cloud Platform (GCP), GraphQL, Jenkins, Julia, Kubeflow, Kubernetes-K8s, Machine learning techniques, MapReduce, MLflow, MLOps tools, Python Programming, PyTorch, REST API, SQL, Streaming APIs, TensorFlow, Terraform

Job Description

• 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  
• 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  

ABOUT US

Bain & Company is a global consultancy that helps the world’s most ambitious change makers define the future.

Across 64 cities in 39 countries, we work alongside our clients as one team with a shared ambition to achieve extraordinary results, outperform the competition, and redefine industries. We complement our tailored, integrated expertise with a vibrant ecosystem of digital innovators to deliver better, faster, and more enduring outcomes. Our 10-year commitment to invest more than $1 billion in pro bono services brings our talent, expertise, and insight to organizations tackling today’s urgent challenges in education, racial equity, social justice, economic development, and the environment. We earned a gold rating from EcoVadis, the leading platform for environmental, social, and ethical performance ratings for global supply chains, putting us in the top 2% of all companies. Since our founding in 1973, we have measured our success by the success of our clients, and we proudly maintain the highest level of client advocacy in the industry.

U.S. Compensation and Benefit Information:

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).

Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

In New York City, the good-faith, reasonable annualized full-time salary range for this role is between $203,000 - $270,000; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level