Data Scientist
Tasks
- Build predictive models
- Create model dashboards
- Deploy low latency ML endpoints
- Develop automated model monitoring workflows
- Engineer data and ML pipelines
- Implement model governance and risk controls
- Productionize batch and streaming ML systems
- Set up ML platform components
- Train and tune machine learning models
- Troubleshoot production issues
Perks/Benefits
Skills/Tech-stack
API Gateway | AWS EventBridge | AWS Glue | AWS Lambda | Airflow | Amazon Athena | Amazon DynamoDB | Amazon EMR | Amazon RDS | Amazon S3 | Argo | Argo CD | Argo Workflows | Artifact store | Aurora PostgreSQL | CI/CD | Classification | CloudWatch | CodeBuild | CodePipeline | Data Drift | Data Drift Detection | Databricks | Databricks DABS | Docker | Drift Detection | ETL | Event Driven | Event-driven architecture | Experiment tracking | Feast | Feature Store | GitHub Actions | GitLab | IAM | Iceberg | Integration Testing | Kinesis | Kubeflow | LightGBM | MLflow | Model Monitoring | Model Registry | MySQL | Parquet | PostgreSQL | PySpark | PyTorch | Python | RBAC | Regression | SQS | SageMaker | Scikit-learn | Snowflake | Snowflake SQL | Snowpark | Step Functions | TensorFlow | UDF | Unit Testing | VPC | Vertex AI | WAF | XGBoost
Education
Roles
Regions
Countries
States
Cities
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