below marketmost similar roles pay hereabove market
This role pays less than
60%
of similar roles. Most pay
$162,000–$227,337
— the blue band above.
At the midpoint, this role pays about
$166k
versus about
$195k
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.
Senior Data Scientist, Algorithms, Lyft Biz joins the Lyft Business team to lead technical initiatives across products like Business Travel, Lyft Pass, and Concierge. This role involves shaping algorithmic roadmaps, defining technical visions, and driving the end-to-end lifecycle of machine learning solutions. You will build production-grade ML systems, real-time inference services, batch pipelines, and feature stores while designing rigorous experimentation frameworks and A/B testing strategies. The position requires deep expertise in supervised and unsupervised learning, ranking systems, probabilistic modeling, and causal inference. You will utilize Python, PyTorch, TensorFlow, scikit-learn, Spark, Snowflake, and Databricks to solve complex B2B problems. Key responsibilities include mentoring junior scientists, optimizing model performance for latency and accuracy, and translating business challenges into scalable algorithmic solutions for enterprise partners.
What does a Data Scientist earn in New York?
Median $173200 from 42 postings across 18 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 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 algorithm reliability.
Optimize model performance across latency, accuracy, cost, and reliability using advanced tuning techniques.
Introduce modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new 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.
C/C++
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