Data Scientist (Experimentation & Personalization)
Tasks
- Build predictive models
- Communicate findings to non-technical stakeholders
- Conduct causal inference analyses
- Define metrics and success criteria
- Design statistical approaches for recommendation and personalization
- Develop offline evaluation framework
- Evaluate counterfactual and replay based policies
- Own experimentation framework
- Quantify trade offs between editorial rules and personalization logic
Perks/Benefits
- Company events
- Health insurance
- Home-office allowance
- Language classes
- Meal allowance
- Remote work option
- Stock options
- Visa sponsorship
Skills/Tech-stack
A/B | A/B Testing | Amplitude | B testing | Bayesian Inference | Causal Inference | Contextual Bandits | Counterfactual evaluation | DBT | Databricks | Difference-in-differences | Growthbook | Interleaving | Learning-to-Rank | Multi-Armed Bandits | Multivariate Testing | NumPy | Pandas | Power analysis | Probabilistic Modeling | Probability | Propensity Score Matching | Propensity score | Python | Recommendation Systems | Regression Modeling | Replay estimation | SQL | SciPy | Scikit-learn | Score Matching | Statistical inference | StatsModels | Uplift Modeling
Education
N/A
Roles
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