Principal Data Science Engineer
Tasks
- Assess data source effectiveness
- Build data models and predictive models
- Collaborate to implement and test production code
- Define KPIs
- Deploy ML APIs in production
- Deploy ML models in cloud environments
- Develop POC studies and calibrate outputs
- Develop data mining and machine learning algorithms
- Develop data processing scripts
- Enhance in house algorithms using ML tools
- Identify airline data opportunities
- Implement ML model monitoring
- Monitor and analyze data accuracy and model performance
- Set up ML model monitoring and autoscaling
- Support customer demos and value benchmarks
- Translate business questions into data analysis
Perks/Benefits
- N/A
Skills/Tech-stack
Artifacts registry | Artificial neural networks | Autoscaling | BigQuery | C++ | CI/CD | Clustering | Containers | Data Warehousing | Decision Trees | Feature Store | GCP | GCP Dataflow | GCP Dataproc | GKE | Java | JavaScript | Kubernetes | ML APIs | ML model monitoring | MLOps | Model Monitoring | Neural Networks | PySpark | Python | R | Regression | SQL | Statistical Tests | TensorFlow | Terraform | Vertex AI
Education
Associate Degree | Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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