Applied Scientist Ii

**About the Role**

**Customer Obsession Data Science** is a group responsible for ensuring every Uber customer support experience is extraordinary. Specifically, this team works to build self-service technology and processes that are deeply integrated with the Uber customer support experience, making it easy for our customers and customer support representatives to get to the right outcome, faster. We are also responsible for building the models and algorithms that help Uber customers answer their own questions or, even better, avoid the need for a customer support experience at all.
- Develop and lead statistical and machine learning efforts for different support systems of Uber. Our projects use machine learning, experimentation, causal learning, time series analysis, natural language processing, and more. We'll give you as many challenges as you can seek and help you grow to tackle more.
- Use large scale data processing such as Spark, Hive, and Uber's proprietary machine learning platform, and more
- Collaborate with engineering to write and implement algorithms in production
- Work with a cross-functional team that includes product management, engineering, operations and others to execute on projects
- Propose and own data analysis (including modeling, coding, analytics, and experimentation) to drive business insight and facilitate decisions.
- Building machine learning models to deliver contextual support content for users by content recommendation and intelligent policy optimization.
- What You Will Need
Ph.D., M.S. or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. (If M.S. degree, a minimum of 4+ years of industry experience required and if Bachelor's degree, a minimum of 5+ years of industry experience as a Applied Scientist or equivalent)
- Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
- Expert 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 like SQL, R, and Spark
- Statistical Modeling
- Machine Learning
- Causal Inference
- Solving multi-arm bandit problems
- Experience in causal inference / causal ML is a huge plus.
- Experience in algorithm development and prototyping
- Experience with productionizing algorithms for real-time systems
- Passion for Uber

At Uber, we reimagine the way the world moves for the better. The idea was born on a snowy night in Paris in 2008, and ever since then, our DNA of reimagination and reinvention carries on. We’ve grown into a global platform moving people and things in ever-expanding ways, taking on big problems to help drivers, riders, delivery partners, and eaters make movement happen at the push of a button for everyone, everywhere.

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.

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.