Senior Product Manager, Earner Intelligence

About the Role

Are you an experienced Machine Learning Product Manager who likes to ship products to end users? Are you looking to build impactful ML products, platforms, and pioneer innovation within Uber? If so, this role might be for you.

You will identify and work on key opportunities to bring more growth and efficiency, leveraging our wealth of data and different ML techniques, while spearheading innovation and building platforms to scale the impact to more use cases. This requires judgment to make difficult trade-offs, blending ML with user experience, and the ability to build simplicity from complexity.

In this role, you will build ML products to power user-facing solutions, and also develop platform tools that are used across teams, with a primary focus on Earners - drivers and couriers.

About the team

We are the Earner Intelligence team, focused on creating ML solutions and platforms to drive and scale efficient growth. We build products to tailor the product experience throughout the earner lifecycle from user acquisition, to conversion, early lifecycle, and retention, while also working with other (“domain”) teams to ship solutions and to scale to more use cases.

The team employs a variety of ML/AI techniques, spanning from causal ML, supervised ML, multi-armed bandits, genAI LLM to deep learning embeddings to build impactful products.

Our goal is to make sure that the earner journey is great at every touch point, that we build trust with Earners, and that we drive efficient growth to Uber’s core business and its strategic bets.

What the Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----

1. Own the product roadmap and lead vision, definition, and execution for building Uber’s strategy on efficient growth and user experience optimization using ML.
2. The team employs a variety of ML/AI techniques, spanning from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLM to deep learning embeddings to build impactful data products.
3. Rethink the journey - deeply understand earners, their motivations and challenges, and keep making their experience better.
4. Model and predict earner behaviors to improve earner experience throughout the onboarding funnel
5. Design recommendation engines to recommend the most relevant earning opportunities and early lifecycle content
6. Develop matching algorithms for driver to driver mentorship program
7. A lot more
8. Innovate - find new ways of using ML to drive efficient growth and user experience improvements with new technologies.
9. Distill vision and strategy for the team, work closely with multi-functional leads to develop technical vision, new methodological approaches, and get all members of cross-functional teams #superpumped.
10. Own your numbers, drive your cross-functional team of engineers, data scientist, product ops and designers to set qualitative objectives and quantitative goals - and overachieve them.
11. Clearly communicate product plans, benefits, and results to leadership.

---- Basic Qualifications ----

1. Minimum of 4 years of Machine Learning Product Management experience.
2. Bachelor's or equivalent experience in Computer Science, Machine Learning, Statistics, Engineering, or other related fields
3. Experience with launching ML-based projects, including data collection and clean up, model training and observability, using distinct ML techniques for different applications.
4. Experience delivering highly successful and creative consumer products with your fingerprints all over them in Search, Recommendation, Targeting, Pricing, Incentives, or related - you're proud of what you've accomplished.
5. Expertise in the design and architecture of ML systems and workflows.
6. Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
7. Data-driven: you always get the data you need, and you can distill it into an insightful story. Most importantly, you leverage data to drive strategic decisions.

---- Preferred Qualifications ----

1. Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.
2. Experience building and productionizing innovative end-to-end Machine Learning systems.
3. Experience in building and optimizing complex user flows
4. Launching products in different international markets

For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 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.