McKinsey is looking to solve industry questions related to how AI can be used for therapeutics, chemicals & materials, and is seeking a data engineer/machine learning engineer to help build and shape its scientific AI offering
Requirements
- Experience in research with a Masters degree with 2+ years or PhD degree with 1+ years of relevant experience in computer science, computer engineering or equivalent experience
- ETL, big data experience and tooling (i.e., PySpark, Databricks)
- Python testing frameworks, data validation and data quality frameworks, data handing (SQL & NoSQL)
- Feature engineering, chunking, document ingestion, graph data structures (i.e., Neo4j)
- CI/CD pipelines, basic K8s (manifests, debugging, docker, Argo Workflows)
- MLflow deployment and usage, GenAI frameworks (LangChain)
- GPU model development / deployment
Responsibilities
- Bringing distinctive data/machine learning engineering & product development competency to complex client problems through part-time staffing on clients
- Supporting the manager of data engineering/machine learning engineering on the development of data/machine learning engineering roadmap of assets across cell-level initiatives
- Productionize AI prototypes/create deployment ready solutions
- Translating engineering concepts and design/architecture trade-offs and decisions for senior stakeholders
- Writing optimized code to advance our AI Toolbox and codify methodologies for future deployment
- Working in a multi-disciplinary team
- Delivering distinctive capabilities, data, and machine learning systems through work with client teams and clients
Other
- Masters degree with 2+ years or PhD degree with 1+ years of relevant experience
- Ability to work with clients and have direct client contact
- Proven experience in translating technical methods to non-technical stakeholders
- Strong ability to write production code and object-oriented programming
- Ability to work in a high performance/high reward culture
