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Uber's Surge team aims to maintain marketplace reliability by balancing supply and demand in real-time through dynamic pricing, generating billions in annual gross bookings and significantly impacting driver earnings.

Requirements

• 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.
• 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++.

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
• Build and productionize innovative end-to-end Machine Learning systems

Other

• 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 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.