Data Scientist
Tasks
- Build train test and validate models
- Deploy models to production infrastructure
- Document models and decision rationale
- Explore agentic and reasoning systems
- Form hypotheses using evidence
- Monitor and improve models in production
- Select and implement machine learning methods
Perks/Benefits
- 401k match
- Flexible work location
- Health insurance
- Paid time off
- Stock options
- Stock purchase plan
- Work from home
Skills/Tech-stack
A/B | A/B Testing | AWS Bedrock | AWS Lambda | AWS SageMaker | AWS Step Functions | Actor-critic | Amazon CloudWatch | Amazon EC2 | Amazon EKS | Amazon S3 | Attention Mechanisms | AutoGluon | B testing | Batch inference | CI/CD | Canary deployments | CatBoost | Confluence | Convolutional Neural Networks | Data Drift | Deep learning | Docker | Embedding Models | Exploration Exploitation Tradeoff | Exploration/exploitation | Feature Engineering | Fine Tuning | Git | Gradient boosting | Hyperparameter Tuning | Infrastructure as Code | Jira | Jupyter | LLM APIs | LightGBM | Long Short | Long Short-Term Memory | MLflow | Machine Learning | Model Monitoring | Model Serving | Model retraining | Model versioning | Multi-Armed Bandits | Neural Networks | Policy gradients | Prompt engineering | PyTorch | Python | Q-learning | RAG | Real Time | Real-time Inference | Recurrent Neural Networks | Reinforcement Learning | Reward shaping | SQL | Scikit-learn | Short Term Memory | Slack | Snowflake | Step Functions | Streaming inference | TensorFlow | Transformers | Weights and Biases | XGBoost | “as-code”
Education
Roles
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