Data Scientist – Experimentation & Causal Inference
Tasks
- Analyze observational data for causal relationships
- Define causal estimation methodologies
- Design Randomized Controlled Trials
- Design causal models
- Document models experiments and analyses
- Ensure experimental design and group allocation
- Estimate causal impact with statistical techniques
- Prepare reports for non-technical audiences
Perks/Benefits
- Collaborative culture
- Flexible work environment
- Global opportunities
- Professional development
- Vibrant community
Skills/Tech-stack
Azure | Azure DevOps | Causal Discovery | Causal Forest | Causal Inference | CausalImpact | CausalML | DAG | DID | Data Pipelines | Data Processing | Databricks | Difference-in-differences | Distributed data | Distributed data processing | DoWhy | Double Machine Learning | EconML | Fixest | Gsynth | Heterogeneity Analysis | Machine Learning | MatchIt | Multiple Testing Control | Multiple testing | Parallel Trends | Potential Outcomes | Power analysis | PySpark | Python | R | Randomized-Controlled Trials | SQL | SUTVA | Sample Size | Sample Size Calculation | Scikit-learn | Selection bias | Statistical Power | Statistical power analysis | StatsModels | Synth | Synthetic control
Education
N/A
Roles
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