Data Scientist II, ML Infrastructure
Tasks
- Build data driven frameworks for feature importance
- Build self serve causal insight tooling
- Design and build centralized ML platform tooling
- Establish methodological standards for measurement
- Own end to end design of production ML systems
- Partner with ML engineers to improve tooling and metrics
- Productionize causal inference methods
- Translate research workflows into production ML pipelines
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Airflow | Causal Inference | Code review | Distributed Computing | Feature importance | Inverse probability weighting | Machine Learning | Propensity score | PyTorch | Python | Ray | Reproducible Machine Learning | Spark | TMLE | Version control | WandB
Education
Regions
Countries
States
Cities
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