below marketmost similar roles pay hereabove market
This role pays less than
60%
of similar roles. Most pay
$160,000–$231,550
— the blue band above.
At the midpoint, this role pays about
$166k
versus about
$196k
for comparable roles.
Based on 240 similar postings.
Employer
About Lyft
Lyft is a transportation network company offering ride-hailing services across the US and Canada.
Lyft currently has
29 open roles
on FindRole.
Listed pay typically runs
$148,000–$185,000
across 24 roles with salary data.
The Senior Data Scientist, Algorithm, Lyft Biz joins the Lyft Business team to lead technical initiatives across products like Business Travel, Lyft Pass, and Concierge. This role involves shaping the technical vision, defining algorithmic roadmaps, and driving execution for data science projects that improve operational efficiency for enterprise partners. You will own the complete lifecycle of algorithmic solutions, including problem formulation, feature engineering, and production deployment of ML systems. Key responsibilities include building real-time inference services, batch pipelines, and experimentation frameworks. You must demonstrate expertise in machine learning, causal inference, and optimization using Python, PyTorch, TensorFlow, scikit-learn, Spark, Snowflake, and Databricks. The work focuses on solving B2B transportation challenges, including corporate travel, healthcare access, and customer experience through scalable APIs and integrations.
What does a Data Scientist earn in California?
Median $214500 from 136 postings across 32 companies.
Lead complex machine learning, AI, and causal inference initiatives across the Lyft Business product suite.
Own the complete lifecycle of algorithmic solutions from problem formulation and feature engineering to deployment and monitoring.
Partner with Engineering to build and scale production-grade ML systems, real-time inference services, and batch pipelines.
Define offline and online metrics, evaluation frameworks, and A/B testing strategies to ensure algorithmic reliability.
Optimize model performance across latency, accuracy, cost, and reliability using advanced tuning and scientific rigor.
Introduce modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities.
Translate complex business challenges into concrete algorithmic solutions for Product, Engineering, and Operations teams.
Mentor junior and mid-level scientists by providing technical guidance and conducting modeling critiques.
What we're looking for
Master’s or PhD in Machine Learning, Computer Science, Statistics, Optimization, or a related quantitative field (or equivalent applied experience).
5+ years of hands-on experience developing, deploying, and maintaining production machine learning models and optimization systems.
Deep knowledge of supervised/unsupervised learning, ranking/decisioning systems, probabilistic modeling, and causal inference.
Strong proficiency in Python, modern ML frameworks like PyTorch, TensorFlow, and scikit-learn, and distributed data systems like Spark, Snowflake, and Databricks.
Hands-on experience building end-to-end ML architectures, including online/batch pipelines, feature engineering, and automated monitoring frameworks.
Demonstrated track record of designing rigorous experimentation strategies, A/B tests, and offline/online validation methodologies.
Proven ability to independently drive multi-project algorithmic scopes and navigate technical ambiguity from ideation to delivery.
Exceptional ability to translate complex technical concepts for non-technical stakeholders and a history of mentoring peers.
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