Senior Product Manager, Earner Intelligence

Uber Uber · Consumer · San Francisco, CA +1 · Product

Product Manager for Earner Intelligence at Uber, focusing on building ML products and platforms for efficient growth and user experience optimization for drivers and couriers. The role involves owning the product roadmap, defining strategy, and working with ML techniques like causal ML, supervised ML, multi-armed bandits, genAI LLM, and deep learning embeddings. Key responsibilities include understanding earner behavior, designing recommendation engines and matching algorithms, and driving the execution of ML-based projects from data collection to model training and observability.

What you'd actually 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

Skills

Required

  • Machine Learning Product Management
  • launching ML-based projects
  • data collection and clean up
  • model training and observability
  • distinct ML techniques
  • delivering highly successful and creative consumer products
  • Search
  • Recommendation
  • Targeting
  • Pricing
  • Incentives
  • design and architecture of ML systems and workflows
  • exploratory data analysis
  • statistical modeling
  • hypothesis testing
  • experimental design
  • Data-driven
  • distill data into an insightful story
  • leverage data to drive strategic decisions

Nice to have

  • optimization techniques
  • reinforcement learning (RL)
  • Bayesian methods
  • causal ML meta learners
  • genAI LLM
  • building and productionizing innovative end-to-end Machine Learning systems
  • building and optimizing complex user flows
  • launching products in different international markets

What the JD emphasized

  • Minimum of 4 years of Machine Learning Product Management experience.
  • Experience with launching ML-based projects, including data collection and clean up, model training and observability, using distinct ML techniques for different applications.
  • Expertise in the design and architecture of ML systems and workflows.
  • Experience building and productionizing innovative end-to-end Machine Learning systems.

Other signals

  • ML products
  • ML platforms
  • scale the impact
  • user-facing solutions
  • platform tools
  • efficient growth
  • earner lifecycle
  • causal ML
  • supervised ML
  • multi-armed bandits
  • genAI LLM
  • deep learning embeddings
  • recommendation engines
  • matching algorithms
  • new ways of using ML
  • launching ML-based projects
  • model training and observability
  • ML systems and workflows
  • optimization techniques
  • reinforcement learning
  • Bayesian methods
  • productionizing end-to-end Machine Learning systems