Staff Machine Learning Scientist, Applied Causal Inference
San Francisco, CA; Sunnyvale, CA; Los Angeles, CA; Seattle, WA; New York City, NY
USD 203K-299K Senior-level Full Time
Tasks
- Apply doubly robust estimation double ML IV diff in diff synthetic controls CUPED and related methods
- Build uplift models and heterogeneous treatment effect models
- Connect experimentation observational data and ML decisioning
- Debug and validate causal claims in a real marketplace
- Design build and productionize causal ML systems
- Design surrogate metrics and early indicators
- Develop counterfactual evaluation frameworks
- Translate causal models into production systems for ranking targeting and recommendations
Perks/Benefits
- 401k plan
- Basic life insurance
- Commuter benefits match
- Disability insurance
- Family-forming assistance
- Medical/Dental/Vision insurance
- Mental health program
- Paid Holidays
- Paid parental leave
- Paid sick leave
- Paid time off
- Wellness benefits
Skills/Tech-stack
CUPED | Causal Graphs | Causal Inference | Contextual Bandits | Counterfactual evaluation | Difference-in-differences | Double ML | Doubly Robust | Doubly-robust estimation | Econometrics | Experiment design | Experimentation | Heterogeneous treatment effects | Instrumental Variables | Machine Learning | Model Evaluation | Off Policy | Off Policy Learning | Off-policy evaluation | Policy Evaluation | Policy learning | Ranking Systems | Recommendation Systems | Robust Estimation | Search & Ranking | Surrogate metrics | Synthetic controls | Uplift Modeling
Education
N/A
Regions
Countries
States
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