Principal Data Scientist
Tasks
- Apply optimization for forecasting and anomaly detection
- Build data and ML pipelines
- Collaborate with domain experts and cross-functional teams
- Conduct root cause analysis
- Deploy machine learning models
- Design APIs and batch scoring pipelines
- Develop machine learning solutions
- Establish performance tracking and alerting
- Frame ambiguous business problems
- Implement model monitoring and drift detection
- Improve quality and yield
- Partner to integrate models into enterprise ecosystems
- Perform exploratory data analysis
- Perform feature engineering and model evaluation
- Productionize decision support applications
- Scale analytics and machine learning solutions
- Support inventory optimization and scheduling
- Translate insights into recommendations
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | Anomaly Detection | Applied Mathematics | Batch Scoring | CI/CD | Cause analysis | Classification | Cloud Computing | Code review | Data Analysis | Data Pipelines | Drift Detection | Exploratory Data Analysis | Feature Engineering | Forecasting | Git | Inventory optimization | MLOps | Machine Learning | Model Drift | Model Drift Detection | Model Evaluation | Model Monitoring | Model retraining | Operations Research | Optimization | Probability | Python | Root Cause Analysis | Root cause | Scheduling | Statistics | Testing
Education
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