The mission of the Surge team is to maintain overall marketplace reliability by balancing supply/demand in real-time through dynamic pricing for Uber
Requirements
- Expertise with Causal Inference, DML, etc...
- Expertise in deep learning and optimization algorithms
- Experience with ML frameworks such as PyTorch and TensorFlow
- Experience building and productionizing innovative end-to-end Machine Learning systems
- Proficiency in one or more coding languages such as Python, Java, Go, or C++
- Experience in combining observational data with experimental data for building causal models
- Experience designing embeddings and combining structural models and regularization techniques for dealing with sparsity
Responsibilities
- Build and train machine learning models with sparse data
- Design experiments and use a variety of techniques for building causal models
- Be a thought leader and help define roadmaps across multiple rider pricing teams
- Build large-scale pricing optimization systems to set prices based on real-time marketplace conditions for Uber's rides products globally
- Work with a mixed team of Engineers, Operations Researchers, and Economists
- Make pricing decisions for each rider session
- Solve network optimization programs
Other
- PhD in relevant fields (CS, Stats, Economics, Econometrics, etc.) with a focus on Machine Learning
- 4+ years of experience in an ML role with an emphasis on data and experiment driven model development
- Strong communication skills and can work effectively with cross-functional partners
- Strong sense of ownership and tenacity toward hard machine-learning projects
- Academic background in Economics or Econometrics
