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Manager II, Science - Marketplace/Membership

Uber

Manager II, Science - Marketplace/Membership

Uber

San Francisco, CA; Sunnyvale, CA

·

On-site

·

Full-time

·

4d ago

About the Role

End-to-end product development from a data, analytics, modeling and experimentation perspective. Specifically, formulating ambiguous business problems, prototyping various machine learning & optimization & causal inference & marketplace dynamics models, producing data insights, and designing & analyzing experiments.

About the Team*(We are hiring for multiple teams)*
We are building the future of Uber's mobility and logistics platforms. Our teams drive innovation across critical areas, including:

  • Delivery Marketplace**: A central pillar of Uber's delivery products, serving as the "brain" of the operation. We drive every decision that enables orders to go from point A to point B - from Uber Eats & Grocery, to newer verticals like Uber Direct and Connect. We're responsible for running an efficient marketplace with dispatch & pricing decisions.- Uber One Membership: Enhancing user experience and growth for Uber One, a fast-growing program providing members with exclusive benefits, best prices, and priority across the platform.**

  • What You'll Do

  • Lead a team of Scientists, including growing and mentoring your team members.

  • Partner closely with cross-functional stakeholders and other Science teams to identify business needs and translate them into technical roadmaps to deliver impactful solutions.

  • Communicate complex findings and recommendations to senior leadership.

  • Solve ambiguous, challenging business problems using data-driven approaches including Causal Inference, Optimization and ML.

  • Own the product development cycle end to end from data and science aspects.

  • Define how our teams measure success, by developing metrics, in close partnership with cross functional partners.

  • Basic Qualifications

  • Ph.D., M.S. or Bachelor's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields.

  • 7+ years of industry experience as an Applied or Data Scientist or equivalent (or 4+ years with Ph.D.).

  • 1+ years managing a team of scientists.

  • Excellent communication and collaboration skills: Able to lead initiatives across multiple product areas and communicate findings with leadership and product teams.

  • Background in at least one programming language (eg. Python, R, Java, Ruby, Scala/Spark or Perl)

  • Experience designing experiments and interpreting the results to draw detailed and actionable conclusions across a variety of key performance indicators

Preferred Qualifications:

  • Ph.D. in Economics, Statistics, Machine Learning, Operations Research or other quantitative fields.

  • 2+ years managing a team of scientists.

  • Experience in leading key technical projects and substantially influencing the scope and output of others.

  • Thought leadership to drive cross-functional projects from concept to production.

  • Experience in large-scale marketplace algorithm design or experimentation platform.

  • For New York, NY-based roles: The base salary range for this role is USD**$216,000 per year**

  • USD**$240,000 per year**.

  • For San Francisco, CA-based roles: The base salary range for this role is USD**$216,000 per year**

  • USD**$240,000 per year**.

  • For Sunnyvale, CA-based roles: The base salary range for this role is USD**$216,000 per year**

  • USD**$240,000 per year**.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity 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.

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About Uber

Uber

Uber develops, markets, and operates a ride-sharing mobile application that allows consumers to submit a trip request.

10,001+

Employees

San Francisco

Headquarters

$120B

Valuation

Reviews

3.1

10 reviews

Work Life Balance

4.2

Compensation

2.3

Culture

3.5

Career

2.0

Management

2.5

45%

Recommend to a Friend

Pros

Flexible hours and schedule

Meeting different people and cultures

Make your own hours

Cons

Inconsistent and low pay

Safety concerns with passengers

Traffic and difficult drivers

Salary Ranges

23,534 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Driver

6,734 reports

$42,142

total / year

Base

$42,142

Stock

-

Bonus

-

$31,192

$56,937

Interview Experience

5 interviews

Difficulty

3.0

/ 5

Duration

14-28 weeks

Offer Rate

40%

Experience

Positive 80%

Neutral 20%

Negative 0%

Interview Process

1

Application Review

2

Online Assessment

3

Recruiter Screen

4

Technical Phone Screen

5

Case Study/Analytics Test

6

Final Loop/Panel Interview

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Case Study

Technical Knowledge