Lead causal inference and marketing mix modeling efforts to measure and optimize marketing investments for Lyft's Growth Products team. Requires 4+ years experience, advanced degree, and expertise in Python, SQL, and causal methods.
136k – 170k
Hybrid4+ YOEData Science
About the role
Responsibilities
Deliver results across the entire lifecycle of data science solutions for Growth: from defining the problem with cross-functional stakeholders to deploying production models that address key business problems.
Own complex domains and develop long-term roadmaps to maximize business impact.
Build statistical pipelines, write production code, and design/analyze experiments.
Participate in the science on-call rotation to ensure automated campaigns operate successfully.
Requirements
Advanced degree in statistics, economics, mathematics, or equivalent industry experience.
4+ years of industry experience in causal inference or data science.
Proven ability to apply statistics to unstructured problems and deliver measurable results.
Deep technical expertise in causal inference and tackling challenging measurement problems.
Expertise in SQL and experience with large-scale data platforms.
Proficiency in Python and working within production coding environments.
Nice-to-Haves
Expertise in marketing mix modeling is highly preferred.
Skills
Causal InferenceMarketing Mix ModelingSQLPythonStatistical ModelingExperiment DesignData ScienceProduction CodeLarge-Scale Data Platforms
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