Data Science Manager, Machine Learning - Lyft Ads

8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.

3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.

Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.

Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.

Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.

Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Great medical, dental, and vision insurance options with additional programs available when enrolled

Mental health benefits

Family building benefits

Child care and pet benefits

401(k) plan with company match to help save for your future

In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off

Data Science, and Machine Learning Engineering for Lyft Media.

Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.

Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.

Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.

Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.

Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.