Data Scientist, Algorithms - Lyft Ads

Lyft

San Francisco, CA, United States
Base: $128,000 - $160,000; bonus/equity: not speci...
On-site
Production-grade machine learning models
Large-scale datasets
Real-time systems
Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement

Job Summary

  • Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
  • Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
  • Lyft offers great medical, dental, and vision insurance options, mental health benefits, family building benefits, and 18 weeks of paid parental leave.

Matching Summary

Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.

Salary

Base: $128,000 - $160,000; Bonus/Equity: Not specified; Benefits: Not specified

Skills & Requirements

Must-have

  • production-grade machine learning models
  • large-scale datasets
  • real-time systems
  • Python and ML frameworks
  • distributed data tools
  • ranking and relevance models
  • optimization or pacing algorithms
  • predictive models for CTR, CVR
  • causal or experimentation-based measurement

Nice-to-have

  • applied machine learning intuition
  • rigorous algorithmic solutions
  • innovation and proactive application
  • collaboration with cross-functional teams

Key Requirements

  • Master's or PhD in quantitative fields or equivalent experience
  • 3-5 years of hands-on ML/applied science experience
  • Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn)
  • Experience with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks)
  • Experience building and evaluating ranking, optimization, predictive, and causal models
  • Understanding of online/offline evaluation techniques (A/B testing, bias correction)
  • Strong communication skills for explaining model behavior to partners

Work Rights

Not specified

Tailored Resume

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