Sr. Applied Scientist, Pricing Science
Tasks
- Build RL optimization platform
- Build uplift models for treatment effect estimation
- Create error detection and price quality guardrails
- Deploy machine learning solutions at scale
- Design transformer neural network architectures
- Develop price prediction models
- Implement causal inference for pricing impact
- Optimize pricing across marketplaces and contexts
- Run A B and quasi experimental experimentation
- Validate hypotheses with statistical analysis
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Applied statistics | B testing | C++ | Causal Inference | Deep learning | Double Debiased Machine Learning | Experimental Design | Hadoop | Instrumental Variables | Java | JavaScript | MXNet | Machine Learning | Multi-Objective Optimization | Neural Networks | NumPy | Objective optimization | Probabilistic Forecasting | Python | Quasi Experimental | Quasi-Experimental Design | R | Reinforcement Learning | SciPy | Scikit-learn | Sequence Models | Spark MLlib | TensorFlow | Transformers | Treatment Effect Estimation | Uplift Modeling
Education
Roles
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