Data Scientist II
Tasks
- Build and ship machine learning models
- Collaborate with product and engineering teams to diagnose live issues
- Deploy and monitor production machine learning pipelines
- Design and run A/B experiments
- Engineer features from large messy data
- Implement logging and observability for model reliability
- Improve measurement and modeling processes
- Model marketplace supply demand matching and pricing
- Monitor model and metric health
- Perform causal inference using quasi experimental methods
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | B testing | Causal Inference | Data Pipelines | Diff DiD | Embeddings | Entity Extraction | Experimental Methods | Feature Engineering | Forecasting | Geospatial analysis | Instrumental Variables | Intent Classification | Kafka | LLM | Logging | Machine Learning | Model Monitoring | Model retraining | NLP | Observability | Operations Research | Power analysis | Python | Quasi Experimental | Quasi-experimental methods | Real Time | Real-time Inference | Reinforcement Learning | SQL | Series forecasting | Spark | Synthetic control | Time Series | Time Series Forecasting
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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