Uber Staff Machine Learning Engineer - Causal Inference 

United States, West Virginia 

295640178

About the Role

- - - - What You Will Do ----

You will work with a mixed team of Engineers, Operations Researchers, and Economists to build large-scale pricing optimization systems to set prices based on real-time marketplace conditions for Uber’s rides products globally.

• 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

- - - - Basic Qualifications ----

• 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++.
• Strong communication skills and can work effectively with cross-functional partners.
• Strong sense of ownership and tenacity toward hard machine-learning projects.

- - - - Preferred Qualifications ----

• 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.
• Experience building elasticity models and user behavioral models
• Proven track record in conducting experiments and tracking models in high-complexity environments.

For New York, NY-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.