Data Scientist
Tasks
- Build and deploy LLM solutions
- Build scalable machine learning pipelines
- Containerize models
- Define data requirements and collaborate on data pipelines
- Deploy models using APIs and microservices
- Design recommendation engines
- Develop machine learning models
- Develop prompt engineering workflows
- Engineer features and evaluate models
- Ensure data quality and governance
- Evaluate recommendation models
- Implement logging alerting and explainability
- Implement model lifecycle management
- Implement retrieval-augmented generation
- Integrate models into CI/CD pipelines
- Monitor model drift and performance
- Perform exploratory data analysis
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Apache Spark | Azure Machine Learning | Azure OpenAI | CI/CD | Collaborative Filtering | Content-Based Filtering | Content-based | Data Warehousing | Databricks | Docker | ETL | Explainable AI | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Hadoop | Hugging Face | Hybrid Recommendation Systems | Hybrid recommendation | Kubernetes | Langchain | Language Models | Large Language Models | Llamaindex | Logging | Machine Learning | Model Monitoring | NDCG | NumPy | OpenAI | Pandas | Personalization | Precision at K | Prompt engineering | PyTorch | Python | Ranking | Recall at K | Recommendation Systems | Retrieval-Augmented Generation | SQL | Scikit-learn | TensorFlow | Vertex AI
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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