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Senior Machine Learning Engineer – Price Transparency
| Company | CVS Health |
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| Location | New York, NY, USA |
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| Salary | $111240 – $222480 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Senior |
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Requirements
- A combination of 3+ years of experience developing production-level software, or contributing to open source with one or more modern languages, such as Python, Java, etc.
- 3+ years of experience with common ML libraries (i.e. Scikit-learn, XGBoost, NumPy, Keras, PyTorch, etc.) and ML algorithms (i.e. clustering, decision trees, boosting, etc.)
- 3+ years of experience with developing Analytical pipelines (using languages such as SQL and PySpark) against Cloud Data Warehouses such as BigQuery, Snowflake or Redshift
- 1+ year(s) of soliciting complex requirements and managing relationships with key stakeholders
- 1+ year(s) of experience independently managing deliverables
Responsibilities
- Design, develop, and operationalize highly complex and large-scale Machine Learning Solutions on the Google Cloud Platform.
- Operationalize the analytical workloads and ML Pipelines using GCP AI/ML Services.
- Support the deployment of advanced algorithms and applications, follow architecture and engineering best practices, and deliver software that is well documented, error free, scalable and performance optimized.
Preferred Qualifications
- Experience with ML operationalization and data and model lifecycle management
- Experience deploying and monitoring analytical assets in batch/real-time business processes
- Experience in cloud environments (GCP preferred): bucket storage, BigQuery, Kubeflow, DataProc, VertexAI, DataFusion
- Understanding of DevOps principles and tools (such as GitHub, Jenkins, Circle CI, Argo CD, Artifact Registry, Nexus)
- Experience in developing distributed data processing pipelines using libraries like Apache Spark or Beam.
- Understanding of dependency management and containerization of applications supporting analytical workloads (i.e. Dockers, GKE)
- Exposure to developing RESTFUL APIs against enterprise data assets and features to support real time operational ML pipelines.
- Experience with FastAPI or Flask is preferred.
- Experience in data visualization tools and libraries
- Experience in an agile development team; helps Tech Lead make decisions
- Experience with complex systems and solving challenging analytical problems
- Effective written and verbal communication skills
