Senior Data Scientist
Tasks
- Build generative AI and LLM powered solutions
- Define success metrics and scope solutions
- Deploy models via CI CD into monitored production
- Design write test and deploy production grade AI ML systems
- Develop fine-tuning workflows
- Engineer scalable ML feature engineering training inference and monitoring pipelines
- Establish experiment tracking version control and model validation
- Implement automated retraining and drift detection
- Implement rag pipelines
- Influence model development validation and deployment standards
- Lead causal inference Bayesian modeling survival analysis and simulation
- Own end-to-end AI/ML lifecycle
- Rapidly prototype and validate proof of concept
Perks/Benefits
- 401k
- Dental insurance
- Disability insurance
- Health insurance
- Life insurance
- Paid time off
- Profit sharing
- Vision insurance
- Wellness program
Skills/Tech-stack
AWS SageMaker | Azure Machine Learning | Bayesian Methods | CI/CD | Causal Inference | Classification | Clustering | Code review | Containerization | DBT | Databricks | Design Patterns | Docker | Drift Detection | Experiment tracking | Feature Store | Fine Tuning | GCP Vertex | GCP Vertex AI | Git | Hugging Face | Kubernetes | LIME | Langchain | Llamaindex | MLflow | Machine Learning | Model Interpretability | Model Monitoring | NumPy | Pandas | Prompt engineering | PyTorch | Python | RAG | Regression | SHAP | SQL | Scikit-learn | Spark | TensorFlow | Time Series | Unit Testing | Vertex AI
Education
Roles
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