Applied Scientist: Machine Learning and Statistics - Rides, Eats, Grocery, Maps, & Experimentation (All Levels)

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Are you interested in building large-scale machine learning, statistical, and generic models which are core to Uber's businesses in Rides and Delivery? What about improving pricing, matching, personalized core trip / order experiences for tens of millions of riders and drivers? If so, then this is the job for you! You'll get to use your modeling skills, ability to solve open and challenging business problems, and obsession and empathy towards customers. You will work closely with Product, Ops, Engineering to take our core product experience and topline business metrics to the next level.

Applied scientists within these teams are responsible for developing advanced machine learning, statistical, economic, and optimization approaches, and for finding science-based solutions to improve performance across Uber's main services (Rides, Eats, Grocery) and enabling technologies (Maps, Experimentation). We are a key part of Uber's cross-functional product development teams, working at every stage from idea to implementation using deep technical expertise.

Note on related roles:

Applied Scientist roles at Uber come with high expectations for technical expertise and focus on expanding the frontier of science-based solutions and decisions. If your interests and experience are oriented toward applying existing experimental, statistical, and machine learning approaches to uncover product insights, solve business problems, or inform strategy, you may be more interested in DS-Product Analytics Role or DS- Strategic and Financial Intelligence DS Role instead.

Teams in this space include:

• Rides: Applied Scientists on Rides develop automation to improve Uber's core ridesharing products, including optimizing Uber's short- and long-term pricing systems, efficiently matching incoming trip requests in the dispatch system, developing innovative incentive schemes that reward riders and drivers for choosing our network, and personalize & improve rider and driver user experiences. Applied Scientists also forecast, monitor, and evaluate marketplace and user behavior using large-scale observational data and rigorous experimentation.

• Eats & Grocery (Delivery): Delivery (Eats & Grocery) is Uber's ambitious and rapidly growing on-demand delivery business. Delivery is a 3-sided marketplace, comprising Eaters, Couriers, and Merchants (mostly Restaurants, but also Grocery, Pharmacy, Alcohol, and others). Applied scientists in this group are responsible for developing advanced machine learning, statistical, and optimization models that power the pricing systems and dispatching / batching algorithms, search and feed ranking, ads & sponsorship, and for improving the efficiency of user acquisition, promotions, courier experience, and merchant onboarding.

• Maps: Applied Scientists work on developing large-scale statistical, optimization, and machine learning models to perform travel time prediction, traffic prediction, location and trip intelligence, route recommendation, navigation, pickup & dropoff search, and recommendation for all Rides, Eats, and Grocery businesses. These models power the core trip experience and critical internal decision systems such as pricing and matching.

• Experimentation: The XP platform at Uber supports novel experimentation designs and measurements for 2-sided and 3-sided marketplaces, powering company-wide experimentation for tens of thousands of experiments. XP is critical in providing reliable, trustworthy and agile experimentation enabling our business and product decisions. Applied Scientists on the team develop cutting-edge improvements to experimentation methodology to increase power and velocity.

What You'll Do

• Develop creative solutions and build prototypes to business problems using algorithms based on machine learning, statistics, and optimization, and work with engineering/product to productionize those algorithms and create impact in production.
• Drive clarity and solve ambiguous, challenging business problems using data-driven approaches.
• Propose and guide the framework of data analysis to drive business insight and facilitate decisions. Establish standard methodologies for data science including modeling, coding, analytics, and experimentation.
• Leverage data to understand product performance and to identify improvement opportunities
• Design product experiments and interpret the results to draw detailed and impactful conclusions
• Communicate with senior management and multi-functional teams
• Provide recommendations to assist quick product ideation and feature launch decisions.
• Build intelligent data-driven products to provide the best user experience

Basic Qualifications

• Ph.D., Master's, or Bachelor's degree in Statistics, Machine Learning, Operations Research, Economics, Computer Science, or other quantitative field. (If Master's degree, 1+ years of industry experience required; if Bachelor's degree, 2+ years of industry experience required)
• Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics

• Knowledge of experimental design and analysis
• Experience with exploratory data analysis, statistical analysis and testing, and model development
• Ability to use a language like Python or R to work efficiently at scale with large data sets
• Proficiency in languages and tools like SQL, Hive, and Spark

Preferred Qualifications

• 5+ years of industry experience working as an applied scientist or similar
• Tech lead experience is a plus
• Experience with productionizing algorithms for real-time systems
• Proficiency in Java, Scala, or Go
• Advanced experience in experimental design and analysis (e.g., A/B and market-level experiments), causal inference