## About the Company
McKinsey & Company brings together multidisciplinary teams to help organizations make data-driven decisions and address complex business challenges. Its People Analytics and Measurement team focuses on using advanced analytics, data science, and measurement methodologies to improve people management and workforce decision-making.
The team works across areas including employee satisfaction, team structure, recruiting, staffing, training, and workforce management while exploring emerging approaches in analytics and technology.
## About the Role
McKinsey & Company is seeking a Machine Learning Engineer to join its Development and Data Science pod within the People Analytics and Measurement team.
This role sits at the intersection of machine learning, natural language processing, MLOps, cloud computing, and software engineering. You will contribute across the full machine learning lifecycle, from research and algorithm development to production deployment and ongoing service management.
Working closely with data engineers, developers, and product managers, you will build scalable and reliable machine learning services that transform large datasets and unstructured information into actionable insights for firm-wide decision-making.
### Key Responsibilities
* Research and develop machine learning and analytics algorithms using internal and external datasets.
* Develop solutions for use cases involving recruitment, training, staffing, team composition, employee satisfaction, and related workforce analytics.
* Design, implement, deploy, and integrate state-of-the-art machine learning services into critical workflows.
* Build and maintain scalable, reliable, and performant data science services for both real-time and scheduled applications.
* Develop NLP tools and services that transform unstructured text into actionable insights.
* Analyze documents such as resumes, job descriptions, surveys, and other firm materials to support recruiting, staffing, and workforce-related use cases.
* Develop reusable NLP, ML Ops, and other technical capabilities for data scientists across the organization.
* Experiment with emerging technologies and methodologies and share knowledge with colleagues.
* Collaborate with data engineers, software developers, product managers, and other stakeholders.
* Communicate analytical processes, technical concepts, and insights clearly to both technical and non-technical audiences.
* Apply software engineering and DevOps best practices to production machine learning systems.
### Required Qualifications
* Bachelor’s degree in a quantitative discipline such as Statistics, Mathematics, Econometrics, Computer Science, Economics, Physics, or a related field.
* Strong analytical and problem-solving skills.
* Experience with natural language processing, machine learning, statistical methods, and optimization.
* Understanding of NLP techniques such as language transformers, large language models, text embeddings, and topic modeling.
* Knowledge of statistical methods including linear and logistic regression.
* Understanding of machine learning methods such as boosted trees and deep neural networks.
* Experience with SQL and database design principles.
* Fluency in Python.
* Strong understanding of software engineering and DevOps practices, including Git and software testing.
* Strong communication skills and the ability to work effectively in a remote environment.
* Customer-focused mindset and commitment to quality.
### Preferred Qualifications
* Master’s or PhD in a quantitative discipline.
* At least 3 years of career experience developing production-oriented systems in Python.
* Knowledge of R.
* Experience with AWS cloud architecture and approximately 2 years of experience establishing, optimizing, troubleshooting, and maintaining cloud environments for performance and stability.
* Experience with infrastructure as code using AWS CloudFormation and/or Terraform.
* Experience maintaining CI/CD automation using GitHub Actions.
* Experience containerizing applications with Docker for service-based architectures.
* Experience with Snowflake or other data warehousing and cloud solutions.
* Experience with graph databases such as Neo4j.
* Experience applying machine learning and NLP solutions to large-scale organizational datasets.
### Technical Environment
The role involves working across a modern machine learning and cloud technology stack, including:
* Python and R
* SQL
* AWS
* AWS CloudFormation
* Terraform
* GitHub Actions
* Docker
* Snowflake
* Neo4j
* Natural Language Processing and Large Language Models
* Machine Learning and Deep Learning
* Data Warehousing and Graph Databases
## Equal Opportunity
McKinsey & Company is committed to fostering an inclusive workplace where individuals can contribute, develop, and succeed. The company values diverse perspectives and provides opportunities based on qualifications, capabilities, and business needs.
Equal employment opportunities are provided regardless of sex, gender, race, color, religion, national origin, age, disability, or other characteristics protected by applicable law.