Data Scientist
Tasks
- Build production analytics workflows and automated pipelines
- Conduct exploratory data analysis and model evaluation
- Deploy machine learning models in production
- Develop and deploy machine learning models for business problems
- Develop and maintain optimization engines
- Implement CI/CD pipelines for machine learning
- Implement MLOps practices including model monitoring and versioning
- Perform data wrangling and feature engineering
- Support GenAI applications using retrieval augmented generation
- Translate business problems into analytical solutions
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon SageMaker | Amazon Web Services | Azure | Azure Machine Learning | CI/CD | Cloud Computing | Cloud platform | Data Analysis | Data Wrangling | Databricks | Exploratory Data Analysis | Feature Engineering | Google Cloud | Google Cloud Platform | Kubeflow | Langchain | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Model versioning | NumPy | Optimization | Pandas | Prompt engineering | PySpark | Python | Retrieval-Augmented Generation | SQL | Semantic Kernel | Snowflake | Statistical modeling | Vector Databases | Web Services
Education
N/A
Roles
Regions
Countries
States
Cities
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