The Surge team at Uber aims to maintain marketplace reliability by balancing supply and demand in real-time through dynamic pricing. This involves building scalable real-time systems to forecast demand, make predictions using ML models, solve network optimization problems, and set pricing for rider sessions, ultimately generating significant gross bookings and contributing to driver earnings.
Requirements
- 4+ years of experience in an ML role with an emphasis on data and experiment driven model development.
- 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.
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.
Other
- PhD in relevant fields (CS, Stats, Economics, Econometrics, etc.) with a focus on Machine Learning.
- 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
- Experience designing embeddings and combining structural models and regularization techniques for dealing with sparsity.
