Data Scientist
Tasks
- Build classical machine learning models
- Communicate insights risks and trade offs to stakeholders
- Define hypotheses success metrics and evaluation plans
- Design build and evaluate generative AI solutions
- Develop large language model pipelines and agent workflows
- Establish offline and online evaluation tests
- Implement RAG pipelines and vector search
- Implement production ready Python code with engineering best practices
- Partner cross functionally with product and engineering teams
- Set up monitoring and traceability for drift and cost latency
- Translate business needs into data science problems
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AUC | AWS | Automated testing | Azure | B testing | Blob Storage | CI/CD | Classification | Clustering | Code Reviews | Containers | Cost Tracking | Data Governance | Data Privacy | Databricks | Embeddings | F1-score | Feature Engineering | Forecasting | Generative AI | Git | Interleaving | Lambda | Langchain | Langgraph | Language Models | Large Language Models | Latency tracking | Machine Learning | Model versioning | Monitoring | NumPy | PGVector | Pandas | PostgreSQL | Prompt engineering | Prompt versioning | PyTorch | PydanticAI | Python | RAG | RMSE | Regression | S3 | SHAP | SQL | Scikit-learn | Spark | Structured Logging | TensorFlow | Time Series | Vector Databases | Vector Search
Education
Roles
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