Lead Product Manager, Global Intelligence & Investments
San Francisco, CA, USA
Posted on Thursday, August 10, 2023
About The RoleAs a Lead Product Manager on the Global Intelligence & Investments team, you’ll own defining the product delivering the roadmap for a set of pivotal new initiatives! We apply our expertise in AI/ML, econometrics, and business strategy to build scalable platform solutions that drive Uber's strategic decisions across a diverse set of markets and product categories. Assessing the success of a pricing or investment strategy is dependent on deeply understanding this context and quantifying the impact with thoughtfully defined success metrics. Doing this effectively, at our global scale, has both interesting and complex potential for astronomical impacts! What The Candidate Will Do
- Define the product thesis and vision for 0->1 strategic initiatives for Uber’s Marketplace. Engage with other marketplace technology teams on a broader vision for the problem space.
- Distill a vision and strategy for the team that generates enthusiasm across cross-functional partners and executive leadership.
- Partner closely with Applied Science and ML Engineering to innovate research, and plan new features. Effectively communicate product opportunities, challenges, plans, benefits and results.
- Engage with our mobility and delivery verticals to analyze business trends, campaign performance, and competitive insights. This is pivotal to improving investment decisions and driving product iterations.
- Maintain, monitor and measure the performance of launched products and funnel takeaways back into product development and innovation.
- Product Experience: Minimum 6+ years of PM experience delivering successful and innovative products with your fingerprints all over them.
- Leadership Experience: Prior experience crafting coherent product visions, multi-year strategies, and roadmaps that your team and the business rallies behind.
- Data Savviness: You can find the data needed and whip it into an insightful story while knowing how to use this data to make decisions and prevent getting stuck in analysis paralysis.
- AI/ML Technical Depth: Ability to effectively engage ML engineers and scientists while setting a bold technical vision. We want to see an understanding of the technology and its capabilities/limitations.
- Applied ML Expertise: Proven track record of addressing business problems with ML learning and getting these capabilities into production.
- Customer Empathy: Ability to know the needs of your users while clearly articulating requirements with a deep understanding of ML.