Senior Data Engineer – Scientific AI Full-time

Your Growth
You will work with cutting-edge AI teams on research and development topics across our life sciences, global energy and materials, and advanced industries practices, serving as a data engineer/machine learning engineer in a technology development and delivery capacity.

With your expertise in computer science, computer engineering, cloud, and data transformation (ETL & feature engineering), you will help build and shape McKinsey’s scientific AI offering. As a member of McKinsey’s global scientific AI team, you will address industry questions on how AI can be used for therapeutics, chemicals, and materials (including small molecules, proteins, mRNA, polymers, etc.).

Your work will involve delivering distinctive capabilities, data, and machine learning systems through collaboration with client teams, playing a pivotal role in creating and disseminating cutting-edge knowledge and proprietary assets, and building the firm’s reputation in your area of expertise.

Your Impact

You will leverage your expertise in data/machine learning engineering and product development to address complex client problems through part-time staffing, support the development of engineering roadmaps for cell-level initiatives, and transform AI prototypes into deployment-ready solutions.

By working directly with client delivery teams, you will ensure seamless implementation of these solutions. You will translate engineering concepts and design decisions for senior stakeholders, write optimized code to enhance McKinsey’s AI Toolbox, and codify methodologies for future deployment.

Collaborating with multi-disciplinary teams, you will ensure the smooth integration of AI/ML solutions across projects, while also coaching and mentoring junior colleagues.

Your qualifications and skills

• Master’s degree with 7-8 years of relevant experience or PhD with 5-7 years of relevant experience
• Experience in research
• Data Engineering Experience (DE Path)
• ETL
• Big Data Experience and Tooling (PySpark, Databricks)
• Python Testing Frameworks
• Data Validation and Data Quality Frameworks
• Data Handling (SQL & NoSQL)
• Feature Engineering
• Chunking
• Document Ingestion
• Graph Data Structures (Neo4j)
• CI/CD Pipelines
• Basic Kubernetes (Manifests, Debugging, Docker, Argo Workflows)
• MLflow Deployment and Usage
• Generative AI (GenAI)