Data Science Manager, Rider App

Lyft Lyft · Consumer · Toronto, ON · Core XP

Lyft is seeking a Data Science Manager to lead a team focused on improving the Rider App experience through algorithm development, machine learning, and experimentation. The role involves shaping vision, driving execution, and partnering with cross-functional teams to enhance rider relationships and expand to new segments. The ideal candidate will have expertise in advanced analytics, ML, causal inference, experimentation, and a proven track record of leading data science teams in fast-paced environments.

What you'd actually do

  1. Lead, mentor and grow a high-performing team of Data Scientists and Analytics, focusing on improving end-to-end Rider App experience (booking → waiting → pickup → in-ride → post-ride) with algorithm development, machine learning, experimentation and advanced analytics.
  2. Partner with Product Managers and Engineering leads to define the vision and roadmap for the Rider App experience (e.g., personalized UI, merchandising strategy), ensuring alignment with overall business strategy.
  3. Lead and execute the data science vision and roadmap for initiatives across Rider App Experience. Raise the bar on scientific rigor.
  4. Establish robust experimentation and causal inference frameworks to measure the business impact of new features in a two-sided marketplace.
  5. Conduct deep analyses of complex, large-scale datasets to uncover opportunities for growth, operational efficiency, and improved rider experience.

Skills

Required

  • SQL
  • Python
  • large-scale data processing tools
  • machine learning frameworks
  • experimental design
  • causal inference
  • statistical methodologies
  • people management

Nice to have

  • consumer mobile apps
  • marketplaces
  • two-sided platforms
  • personalization
  • content recommendation
  • uplift modeling
  • lifecycle management

What the JD emphasized

  • advanced analytics, machine learning, causal inference, experimentation
  • leading product data science teams
  • experimental design, causal inference, machine learning, and statistical methodologies
  • applying them to high-stakes product or marketplace decisions
  • personalization, content recommendation, uplift modeling, or lifecycle management

Other signals

  • improves Rider App experiences
  • algorithms and models that power both internal systems and customer-facing products
  • algorithm development, machine learning, experimentation and advanced analytics
  • personalized UI, merchandising strategy
  • data science vision and roadmap for initiatives across Rider App Experience
  • experimentation and causal inference frameworks
  • machine learning, optimization, or causal inference
  • applying them to high-stakes product or marketplace decisions
  • personalization, content recommendation, uplift modeling, or lifecycle management