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
Are you interested in working at the intersection of applied quantitative research, engineering development, and data science? Do you have an interest in developing and applying quantitative solutions uniquely challenging problems on Uber Eats? If so, then this is the job for you.
What You'll Do / What You'll Need / Bonus Points / About the Team
What you'll need
We are looking for people with advanced quantitative degrees who are comfortable enough with research methodologies that they can address abstract business and product problems with extreme precision, and who have the enthusiasm and initiative necessary to deliver those answers at Uber's fast pace. You should also have demonstrable programming skills and be comfortable with the engineering development process.
* Must have 1+ years industry experience outside of academic or internship setting. Prior research, data science modeling, or engineering experience in the aforementioned domains
* Superb quantitative background (e.g. statistics, math, machine learning, operations research, economics, EECS, etc.): Graduate degree required and PhD preferred
* Familiarity with technical tools for analysis - Python (with Pandas, etc.), R, SQL, etc.; previous software engineering background a plus
* Research mindset with bias towards action - able to structure a project from idea to experimentation to prototype to implementation
* Passionate and attentive self-starters, great communicators, amazing follow-through - you have a great work ethic and love the responsibility of being held accountable for the results
About the Team
Uber Eats Data Scientists help solve the most challenging problems related to Uber's ambitious and rapidly expanding on-demand food delivery businesses, which currently operates in more than 45 countries globally and is the largest outside of China. These fascinating and difficult problems include personalized search and recommendation for restaurants and dishes, travel and food preparation time prediction, text mining and natural language processing, demand and supply forecasting, growth and spend optimization, dynamic pricing, dispatch and routing optimization, and many more. Below is a list of sub domains within the team:
* Eater (SF) | From new user acquisition, to existing user engagement, to churned user resurrection, the eater team builds intelligent data-driven products to provide the best user experience. The Eater team is responsible for shaping the business with our expertise in machine learning (including learning to rank, deep learning and NLP), optimization, causal inference, statistics, and a passion for connecting everyone with their favorite food. The challenges the Eater team tackles include: New user acquisition spend optimization, messaging and push notification relevance, search engine optimization (SEO) and search engine marketing (SEM), personalized restaurant and dish recommendation, search relevance and food knowledge platform, appeasement and refund optimization, user conversion and churn modeling.
* Restaurant (NYC) | The restaurant team aims to increase restaurant selection on Uber Eats while setting up restaurant partners for success. We leverage statistics and machine learning techniques to optimize the experience of restaurants at different life cycles. From onboarding, to menu creation, marketing, and order experience, we empower them with tools to increase demand and maintain a frictionless interaction with the platform.
* Courier (SF) | We strive to create a stress free courier experience at every point in their lifecycle, down to the nuances of individual deliveries. We utilize machine learning and statistical techniques to optimize courier onboarding, the on-trip experience, further our understanding of how couriers move within and across a city, and power the models to guide our couriers on how to plan their day and increase their earnings potential. Through our segmentation and marketing efforts, we are also building out our one-of-a-kind loyalty program, to recognize and reward couriers for their commitment and quality of service. We continually optimize for retainment and engagement of our partners.
* Marketplace - Logistics and Marketplace Intelligence (NYC) | From dispatch and routing optimization to predicting food delivery time, the Logistics and & MI team provides DS solutions to create the best customer experience on all sides of the Eats marketplace in the most efficient way. The team also provides marketplace management solutions using state of the art machine learning techniques and our dispatch simulator.
* Marketplace - Pricing (SF) | The pricing team develops algorithms to find the perfect price every time an eater or courier makes a decision. For eaters, we design structural delivery fees, targeted promotions, and real-time reliability pricing. For couriers, we design engagement incentives, positioning incentives, real-time surge, and trip-level pricing. The pricing team uses elements of modeling, causal inference, forecasting, and optimization to design prices that dynamically align customer's and partner's interests with maximizing value created by the marketplace.
Uber provides a mobile application connecting passengers with drivers for hire.