Machine Learning Engineer

Key Skills

AWS, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Design, Docker, Effective communication skills, Kubernetes-K8s, Large data sets, Machine learning techniques, MLOps tools, Natural Language Processing (NLP), Python Programming, R Programming

Job Description

At McKinsey, our most valuable resource is our people. As such, we have built a world class team dedicated to optimizing people management and facilitating data driven decision making, including strategies to improve employee satisfaction, team structure, employee management, recruiting decisions and other high impact use cases.

The People Analytics and Measurement team is globally respected as a leader in its field. You will be provided the opportunity for in-depth exposure to new, emerging methodologies through hands-on client projects, e-learning, certifications, live boot camps and global analytics conferences. This team works in one of the fastest growing disciplines in analytics.

You will be part of the development and data science pod. You will contribute to a diverse, curious, entrepreneurial and innovative team that operates at the intersection of natural language processing, machine learning, ML ops, and cloud computing. Each member of the team is motivated by personal and technical growth and enjoys a highly autonomous working environment along with team oriented projects.

WHAT YOU'LL DO

You will operate at the intersection of Machine Learning and Natural Language Processing. 

You will be supported and encouraged to become a full stack data scientist/machine learning engineer, and you will be given ownership across the entire machine learning lifecycle with a strong emphasis on software engineering principles to deploy your models into production. You will work with data engineers, developers, and product managers to build scalable, reliable, and impactful services. You will be creating and maintaining stable, performant data science services (real-time and scheduled) and applications that will deal with prescriptive analytics pre-dominantly which will help in driving firm decisions on the basis of large data-sets within the firm.

Your specific responsibilities will include:

• Data Science Research: You will leverage firm and external data-sets and develop algorithms in creative ways to support analytics use-cases across the following domains and more - recruitment, training, team composition, staffing, satisfaction of your colleagues, etc.
• Machine Learning Services: You will design, implement, deploy into the cloud, and integrate state-of-the-art machine learning services at scale in production into critical firm workflows that need to be availed for stakeholders and users to serve above use-cases.
• Natural Language Processing: You will build self-serve tools and services to analyze and to distill unstructured text data into actionable insights and communicate those insights to senior stakeholders. For example, you will analyze unstructured text like resumes, job descriptions, surveys, and other firm documents to help with use-cases in recruiting, staffing, etc.
• Capability Building: You will develop NLP, ML Ops, and other capabilities for data scientists across the firm to use. You will have the freedom and autonomy to experiment with cutting edge and empower your colleagues with what you have learned.

QUALIFICATIONS

• BS (Master's or PhD strongly preferred) in a quantitative discipline (e.g., statistics, mathematics or econometrics, computer science, economics, physics or any related field)
• Experience with SQL, as well as a firm understanding of the principles of database design is preferred
• Theoretical and applied understanding of natural language processing techniques (e.g. language transformers/large language models, text embeddings, and topic modelling), statistical methods (e.g. linear and logistic regression), machine learning methods and optimization (e.g. boosted trees and deep neural networks), and graph algorithms
• 2 years experience or exposure to establishing, optimizing, troubleshooting and maintaining cloud architecture for performance and stability (in AWS); specifying infrastructure as code, primarily using AWS CloudFormation and/or Terraform; maintaining CI/CD automation, primarily using GitHub actions; containerizing code for service based architecture, using Docker
• Communication skills and work ethic geared to working in a remote environment
• Fluency in Python with knowledge of R is preferred; at least 3 years career experience developing production oriented systems in Python is strongly preferred
• Strong analytical and problem solving skills; ability to clearly describe analytics processes and concepts in your area of expertise to both technical and non-technical audiences
• Understanding of software engineering and dev-ops best practices such as version control (Git), testing, etc.
• Experience with Data Warehousing/Cloud Solutions like Snowflake and Graph Databases like Neo4j is preferred but not required
• Distinct customer focus and quality mindset