Applied Scientist / Senior Applied Scientist - Pricing and/or Econometrics at Uber (San Francisco, CA)

| San Francisco, CA
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About CAS
The Core Analytics & Science Team (CAS) is Uber's largest data and applied science organization, covering 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 analytic, statistical, and algorithmic expertise.
Here's what's in it for you! As an Applied Scientist in the CAS organization, you have a unique opportunity to use your quantitative skills in statistics, machine learning, operations research, and/or economics. You'll answer high impact open questions, prototype cutting edge algorithms, engage in large scale experimentation, and drive business insights through data. You will be collaborating closely with Product Managers, Engineers, other Applied Scientists and Data Scientists on a large part of the Uber products and services that have become ingrained in the daily lives of our customers and partners.
Pricing and/or Econometrics Applied Science
Here are some of the teams that make it happen:
  • Eats: Uber Eats is Uber's ambitious and rapidly expanding on-demand food delivery business currently operating in more than 45 countries globally.
    • Pricing: Maximize efficiency and maintain reliability in the three-sided marketplace (eater, courier/delivery partner, and restaurant) of Uber Eats. Our algorithms operate globally at a scale of hundreds of thousands of restaurants, millions of delivery partners, tens of millions of eaters, and billions of dollars per year.
  • Rides: Rides Applied Science at Uber uses data to improve and automate all aspects of Uber's core ridesharing products.
    • Pricing: Responsible for modeling rider and driver behavior,setting prices to optimize long run network efficiency, and dynamically adjusting those prices to ensure balance & reliability.
    • Rider/Driver/Supplier Incentives: Sends rider, driver, and supplier incentives and designs Subscription and Rewards programs. These teams build production ML models, run and analyze network-level and A/B experiments.

What You'll Do
  • Build statistical, optimization, and machine learning models for applications including pricing, targeting, and experimentation.
  • Work with engineers and product managers to turn data science prototypes into robust, reliable solutions.
  • Present findings to business leaders to inform decisions.
  • Solve ambiguous, challenging business problems using data-driven approaches.
  • Work closely with multi-functional leads to develop technical vision and drive team direction.
  • Establish standard methodologies for data science including modeling, coding, analytics, optimization, and experimentation.
  • Communicate with senior management and multi-functional teams.

Basic Qualifications
  • Ph.D., MS or Bachelors degree in, Statistics, Economics, Machine Learning, Operations Research, Computer Science or other quantitative field. (If M.S. degree, a minimum of 1+ years of industry experience required and if Bachelor's degree, a minimum of 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
  • Advanced experience in experimental design and analysis (e.g., A/B and market-level experiments), as well as causal inference
  • Experience in algorithm development and prototyping
  • Experience with causal ML methods is a plus
  • Technical leadership experience and passion for mentoring
  • Experience productionizing algorithms for real-time systems
  • Expertise in convex programming, computational optimization, and operations research
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An Insider's view of Uber

What’s the vibe like in the office?

When I went into the office for my final interview with Uber, I had the same feeling that I did when I stepped onto my college’s campus for the first time: it just felt like the right fit. The office was high-paced but also relaxed and you could immediately tell that people were friends and genuinely enjoyed being there.

Paige Sammarco

Account Executive, Uber Eats

What kinds of technical challenges do you and your team face?

One of the big challenges today with experimentation is around guaranteeing correctness, especially for small changes to ensure confidence in results. Was that change the cause of new behavior? Did other experiments get in the way? It all comes down to how accurately you can detect small changes within consumer behavior.

Azarias Reda

Head of Uber's Experimentation API team

What makes someone successful on your team?

"It’s not just about the individual contributor. The most successful people are the ones learning from others. On my team, I make sure that everyone shares best practices and we foster a collaborative culture. So when you’re on a call, you’re never really alone. And that applies to everyone."

Ali Faivus

Head of Mid-Market Sales

How do you empower your team to be more creative?

We make sure we don’t ship org structures, but rather aligned products. How can our products complement one another, building upon each other to achieve our primary goals? Whether it’s scheduling, routing, predictive analytics, or operational excellence, we are acting as one, and smartly leveraging our domains and strengths.

Joe Chang

Director of Engineering, Uber Freight

How does your team reward individual success?

I believe recognizing someone’s contributions are a big part of team play. On our weekly meetings, we always start with a shout-out, and it’s amazing how this simple topic stimulates the team to recognize small victories and accredit colleagues for their accomplishments. This brings our team together and fosters a more collaborative environment.

Silvia Penna

Sr Manager, Central Operations

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