Uber is looking to solve the problem of optimizing its marketplace by making difficult trade-offs, blending algorithms with human resourcefulness, and building simplicity from complexity to create a cost-efficient marketplace for matching supply and demand.
Requirements
- PhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
- 4 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling
- Proficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++)
- Experience with any of the following: Spark, Hive, Kafka, Cassandra
- Experience building and productionizing innovative end-to-end Machine Learning systems
- Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design
- 5+ years of industry experience in machine learning, including building and deploying ML models
- Publications at industry recognized ML conferences
Responsibilities
- Build statistical, optimization, and machine learning models
- Develop innovative new earner incentives that earners for choosing our network and optimizing Uber's new earner incentives spend
- Optimize Uber's background check spend and onboarding funnel
- Design recommendation engines to recommend the most relevant earning opportunities and early lifecycle content
- Develop matching algorithms for driver to driver mentorship program
- Model and predict earner behaviors to improve earner experience throughout the onboarding funnel
- Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction
Other
- PhD or equivalent experience
- 4 years minimum of industry experience
- Collaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product
- Work with cross-functional teams(product, science, product ops etc)
- Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office
