2022 PhD Internship Applied Scientist

Website Uber

Job Description

We’re looking for Ph.D. candidates to intern during summer 2022 (12 weeks). You will be embedded in a team and work closely with other applied scientists, engineers, and product managers under the supervision of an applied scientist on that team.

About the Role

We are hiring interns to work on the Global Marketing, Freight, Policy Research, and Core Analytics & Science teams. Read on to learn more about these three teams.

(1) The Core Analytics & Science (CAS) team covers both of Uber’s main lines of business as well as the underlying platform technologies that power those businesses. We are a key part of Uber’s cross-functional product development teams, helping to drive every stage of product development through data analytic, statistical, and algorithmic expertise. Here are the subteams within CAS that make it happen:

Delivery: Uber Eats is Uber’s ambitious and rapidly growing on-demand food delivery business currently operating in over 45 countries globally and is the largest outside of China. Eats is a 3-sided marketplace, comprising Eaters, Delivery Partners, and Restaurants. Eats interns will work on exciting and impactful projects that advance our algorithms and discover insights to improve engagement with eaters, merchants, and couriers. Projects include optimizing our eater pricing systems and matching algorithms, modeling and predicting eater and courier behavior on the platform, and improving the efficiency of user acquisition, promotions, courier experience, and merchant onboarding.

Rides: Rides Data Science at Uber uses data to improve and automate Uber’s core ridesharing products. Rides interns will tackle problems such as optimizing Uber’s short and long term pricing systems; efficiently matching incoming trip requests in Uber’s dispatch system; developing innovative incentive schemes that reward riders and drivers for choosing our network; developing algorithms and experimentation to make our rider and driver experience stand out. We also forecast, monitor, and evaluate all aspects of our marketplace and user behavior using both large scale observational data and meticulous experimentation.

Platform: Platform Data Science develops the common systems and technical foundations that power Uber’s trip experiences externally and core decision making systems internally. Interns on the Platform team will work on building statistical, machine learning, and optimization models for problems like travel time estimation, route optimization, search ranking, pickup and dropoff experience optimization and personalization, experimentation design for the two/three sided marketplace, and competitive intelligence for pricing strategies across the company.

(2) The Policy Economics & Research teams conduct research that forms the foundation for business decisions and our policy positions and helps policymakers, thought leaders, and the general public gain a better understanding of our platform. This team is led by the Chief Economist, and we are responsible for addressing the thorny policy and business challenges impacting Uber globally. As a data scientist intern on this team, you would be encouraged to bring analytical and empirical rigor to bear on important questions relating to: the changing nature of work and the economics of marketplaces (Policy Economics); the impact that our pricing, matching, and safety products have on users from underserved communities (Marketplace Fairness); and Uber’s impact on urban mobility and transportation systems (Mobility Research).

(3)  Marketing data science informs decisions across Uber’s global marketing efforts, accelerating both demand and supply growth worldwide. We use advanced statistical modeling, machine learning, or data mining techniques in a scalable manner including large scale data processing such as Spark, Hive, and Uber’s proprietary machine learning platform, and more. Interns on the team will work on projects such as:

Assisting in prototyping causal inference methods and econometric models to inform the efficiency of our marketing spend
Building propensity models to aid in the growth and profitability of different Uber products (Uber Rides Pass, Eats Pass, etc.)
Architecting a recommendation system to understand cuisine preferences at a user-level
Standardizing marketing models and enhancing their scalability and stability through platformization
Identifying and implementing improvements to our bid, budget and creative optimization algorithms on Uber’s advertising channels like Google, Facebook, etc.

What You’ll Do

Work with your mentor closely to scope a project, define the problem, and develop and prototype the solution using data-driven approaches
Work with engineers and product managers to turn data science prototypes into robust, reliable solutions
Present findings to business leaders to inform decisions
Establish standard methodologies for data science including modeling, coding, analytics, optimization, and experimentation
Conduct experiments to advise product decisions

Basic Qualifications

Current Ph.D. student majoring in Computer Science, Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields
Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
Experience with exploratory data analysis, statistical analysis and model development
Proficiency in languages like SQL, R and Python

Preferred Qualifications

Anticipated graduation between winter 2022 – summer 2023
Ability to communicate effectively with both technical and business partners
Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation
Experience in experimental design and analysis (e.g., A/B and market-level experiments), as well as causal inference
Independence, excellent communication, and outstanding follow-through – you energetically tackle your work and love the responsibility of being individually empowered

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To apply for this job please visit university-uber.icims.com.