Sr. Data Scientist
Tasks
- Break down metrics into actionable segments
- Build clustering models to identify user segments
- Build dashboards to monitor metric components
- Build segmentation models for pricing strategy
- Build tools for experiment analysis
- Conduct funnel and ecosystem analyses
- Define success metrics for engineering teams
- Design measurement strategy and experimentation best practices
- Evaluate tradeoffs between metric changes
- Improve decision velocity using experimentation and causal inference
- Perform cohort and segmentation analysis
- Perform strategic data analysis
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Attribution windows | B testing | Causal Inference | Clustering | Dashboarding | Data Contracts | Data Lineage | Difference-in-differences | Event taxonomy | Experiment design | Guardrail design | Heterogeneous Treatment Effect | Hive | Machine Learning | NumPy | Pandas | Power analysis | Presto | Propensity Score Matching | Propensity Score Weighting | Propensity score | Python | SQL | Scikit-learn | Score Matching | Spark SQL | StatsModels | Variance reduction
Roles
Regions
Countries
States
Cities
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