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2019 University Graduate - Data Scientist - Rides

Data Science, University à San Francisco, CA

At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.

 

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.

About the Role

We’re looking for Data Scientists to contribute to Rides Data Science, which encompasses all teams responsible for working on moving people from A → B.

What You’ll Need

  • Masters or PhD with an (anticipated graduation in 2019) in areas of focus within empirical modeling (Machine Learning and Statistics), marketplace efficiency (Economics, Operations Research & Operations Management), utilization of rich geospatial data to better understand mobility (Transportation Engineering), or Computer Science students who have a strong interest in these problem spaces
  • Experience in modeling and algorithm development
  • Coding proficiency and the ability to develop statistical analysis and prototype algorithms in Python or R
    Proficiency in SQL
  • Ability to communicate effectively with both technical and business stakeholders

About the Teams

  • Control | Take a holistic view of Uber’s two-sided marketplace to understand how different product levers (e.g. rider and driver pricing, promotions, matching) affect its efficiency, growth, and ability to generate value. Evaluate the balance of supply and demand across time and geography, investigate how Uber’s products shape riders’ expectations and behavior over time, assess how drivers respond to variation in their earnings, and examine how potential regulation changes could impact the health of the marketplace. Leverage techniques from a variety of disciplines, including operations research, statistics and economics, to address these open-ended questions.
  • Driver Trip Experience | Own the full trip experience, from the moment the driver accepts a trip up until the rider leaves the car. Build machine learning models that enable a stress-free trip experience for drivers and riders. This includes detecting driver progress, pickup arrival, and on-trip problems. Conduct research in areas including sequence-to-sequence modeling for drivers' interactions with the app, representation learning for the pickup phase of a trip, and probabilistic models of driver intent.
  • Driver Pricing | Develop advanced and real-time pricing algorithms and driver tools that shape how drivers plan, move, and earn. Use elements of modeling, causal inference, forecasting, and optimization to design prices that dynamically align driver's interests with those of riders and Uber's business.
  • Forecasting | Stand upstream of the marketplace optimization flow and generate time series and machine learning models for quantities like trips, demand, and supply. Empower downstream optimization decisions such as pricing and incentive allocation with these forecasts of varying levels of spatial/temporal granularity and different forecast horizons. Capture calendar patterns and shocks (holidays, events, and weather) using a combination of classical time series and statistical and machine learning approaches in a scalable way.
  • Global Intelligence | Develop advanced statistical/machine learning algorithms to model the volatile market dynamics and rider/driver behaviors among the key Uber markets. Such algorithms and models are essential to the key decision engine that optimizes Uber’s network efficiency in global markets. Existing team members come from a diverse background in Machine Learning engineering, Statistics, Econometrics, Math, Operation Research.
  • Investments | Manage the macroeconomics of our rides marketplace. Every year, Uber spends billions of dollars to influence our marketplace toward healthy growth by deploying a variety of incentives and pricing levers. Develop the algorithms that decide how to invest across all our levers, across all our markets, and what it means to operationalize for healthy, more profitable growth. Interact with many other teams and gain a working knowledge of the many aspects of the massive dynamic system that is our Marketplace.
  • Matching & Shared Rides | Work on the backend algorithms that govern how riders and drivers are matched together across Uber's network, from assigning a car for an UberX dispatch to deciding when to have two riders share a ride together in UberPOOL. Blend optimization, machine learning and algorithmic techniques to deliver efficient, scalable solutions for Uber's network.

  • New Mobility | Build NeMo’s data science infrastructure from the ground up, as you join a brand new team within Uber seeking to complement and expand the business into bikes, scooters, and transit. Drive key decisions across pricing optimization, rider and worker incentive structures, matching, multi-modal route planning, and more. Become a key early contributor and lead decision making with data as your expertise will be critical for shaping the program’s success.
  • Rider | Build products that create an awesome experience for people who ride with Uber including core experiences, airports, events, and business.
  • Rider Engagement | Design rider incentive programs that grow, retain and engage our riders. We create production machine learning models, analyze network level experiments and use mechanism design principles to design incentives, measuring heterogeneous treatment effects, long term effects and marketplace effects. Run real-time causal machine learning models for promotion targeting, and help design the new subscription or loyalty program.
  • Rider Pricing / Rider Fares | Develop the algorithms that set rider prices globally to optimize short and long run network efficiency and reliability. Build, deploy, and analyze large-scale real-time algorithms, from machine learning models predicting travel times and demand patterns, to the optimization models responsible for dynamic pricing.
  • Uber 4 Business | Uber for Business is on pace to being Uber’s next billion dollar business with 65,000+ businesses around the world already on board. Come revolutionize Uber’s B2B strategy, including but not limited to enterprises that need a seamless global travel solution to local businesses that want to provide courtesy rides to customers, or clinics providing reliable rides for patients. Build machine learning models to identify and differentiate business trips from personal trips. Run causal inference analyses to drive incentive structure optimization, intelligent booking, product recommendation, leads ranking, churn analysis and more. Optimize the business traveler experience and nudge the correct users to adopt our business class products.
  • Uber PRO | Build the new, one-of-a-kind loyalty program to reward our drivers for their commitment & service, and help them build toward their future. Develop robust models and algorithms to power differentiated driver earnings and experiences. Build machine learning models for targeted promotional rewards and policies. Research the effects of cognitive, emotional and cultural factors on drivers' behaviors and economic decisions. Design and analyze network level experiments to measure short-term and long-term effects, and optimize our program structure and reward offerings. Check us out in the news(https://www.theverge.com/2018/11/1/18047746/uber-driver-high-performing-earnings-free-college-tuition-pro)!

Déclaration de confidentialité pour les postulants

Chez Uber, nous ne faisons pas qu'accepter la différence : nous la célébrons, nous la soutenons et nous l'encourageons pour nos collaborateurs, nos produits et notre communauté. Uber est fier d'offrir les mêmes opportunités à chacun. Nous nous engageons à offrir à tous les mêmes opportunités, quels que soient, par exemple, l'origine, la religion, l'identité et orientation sexuelle, l'âge, la nationalité, le statut marital ou le handicap.