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Have you ever ordered a car service on Uber, and when the ride arrives, wondered how it got to you so fast? Ever ordered food on UberEats and wondered where the driver was before receiving your order and how long it took to get to the restaurant or if your order was ready when the courier arrived? Ever wondered why your grocery delivery from Uber always has the best apple picked? If so, Uber is for you. In our Sciences division, we strive to make magic within Uber’s marketplace. This requires judgment to make difficult trade-offs, blending algorithms with human resourcefulness, and the ability to build simplicity from complexity. When we get the balance right for everyone, Uber magic happens. We build systems to peer into the future to craft the most cost-efficient marketplace for matching supply and demand. We are passionate about using innovative economics, machine learning, and scalable distributed software that automates and optimizes every aspect of this intricate dance between participants of the marketplace. We are involved in every stage of the product development cycle and use data to inform product decisions, build models to power our solutions, and also develop platform tools that are used across teams with a primary focus on Mobility and Delivery. We work with millions of earners across the globe to make this magic happen and want you to join us!
Job Responsibility:
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
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
Requirements:
Masters or PhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
7 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
Experience working with cross-functional teams(product, science, product ops etc)
Nice to have:
8+ years of industry experience in machine learning, including building and deploying ML models
Publications at industry recognized ML conferences
Experience in modern deep learning architectures and probabilistic modeling
Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM
Expertise in the design and architecture of ML systems and workflows
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp