Lyft
Senior Data Scientist, Algorithm, Lyft Biz
$148K–$185K
San Francisco, CA +2 · Senior · Full-time · Posted Jul 6, 2026
- Location
- San Francisco, CA
New York, New York, United States
San Francisco, California, United States - Experience
- 5+ years of hands-on industry experience developing, deploying, and maintaining production machine-learning models and optimization systems.
- Education
- Master’s or PhD in Machine Learning, Computer Science, Statistics, Optimization, or a related quantitative field (or equivalent applied experience).
About the job
Leads technical initiatives across Lyft Business products, developing and deploying machine-learning algorithms, experimentation frameworks, and analytics to support growth, efficiency, and enterprise partner value.
What you'll do
- Lead algorithmic and causal-inference projects; own solutions from problem formulation through deployment, monitoring, and iteration.
- Partner with Engineering to build production ML systems and define evaluation metrics, experimentation strategies, and model improvements.
- Guide cross-functional work, shape technical roadmaps, and mentor other data scientists.
What they ask for
- 5+ years developing, deploying, and maintaining production machine-learning models and optimization systems.
- Deep knowledge of supervised and unsupervised learning, ranking and decisioning systems, probabilistic modeling, and causal inference.
- Proficiency in Python, modern ML frameworks, and distributed data systems; experience building end-to-end ML architectures and rigorous experiments.
- Demonstrated ability to lead multi-project scopes, communicate technical concepts to non-technical stakeholders, and mentor peers.
Pay
$148,000 - $185,000 USD base salary
Tools
Python, PyTorch, TensorFlow, scikit-learn, Spark, Snowflake, Databricks
AI focus
Leads Machine Learning and AI initiatives, including production models, optimization, experimentation, and potential use of reinforcement learning and graph-based methods.
Model-written summary · the original posting is authoritative