Data Scientist - Decisions, Mapping

Lyft Lyft · Consumer · Toronto, ON · Mapping

Data Scientist on the Mapping team at Lyft, focusing on improving recommended routes and travel time estimations for rideshare drivers. The role involves leveraging spatial data, building a mapping product, and using data science to shape mapping products and business decisions. Key responsibilities include identifying opportunities, designing experiments, and measuring feature impact.

What you'd actually do

  1. Leverage data and analytic frameworks to identify opportunities for growth and efficiency
  2. Partner with product managers, engineers, and operators to translate analytical insights into decisions and action, and implement products to drive business goals
  3. Define and implement decision frameworks, measurement strategies, and scientific methodologies that bring consistency and rigor to business decisions and forecasts, balancing opportunity and uncertainty
  4. Deliver integrated, high-quality analytical outputs spanning multiple projects while navigating ambiguity, cross-team dependencies, and open-ended scope
  5. Design and analyze online experiments; communicate results and act on launch decisions

Skills

Required

  • SQL
  • Python or R
  • PyTorch, TensorFlow, Keras
  • Online experimentation
  • Statistical analysis
  • Communication
  • Critical thinking
  • Prioritization

Nice to have

  • Machine learning techniques
  • Reinforcement learning
  • Personalization
  • Segmentation
  • Metric design
  • Causal analysis
  • Behavioral analytics
  • Decision frameworks
  • Measurement strategy
  • ETL pipelines

What the JD emphasized

  • 3+ years experience in a data science role or analytics role
  • Demonstrated ability to own multi-project analytical scopes with ambiguous problem definitions and cross-functional integration
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization
  • Proficiency in SQL - able to write structured and efficient queries on large data sets
  • Experience in programming, especially with data science and visualization libraries in Python or R, and machine learning libraries such as PyTorch, TensorFlow, Keras
  • Experience in online experimentation and statistical analysis, and communicating results and recommendations to senior stakeholders
  • Strong communication, critical thinking, and prioritization skills, including the ability to challenge assumptions, propose alternatives, and balance short-term vs. long-term tradeoffs