Principal Data Scientist

Role: Principal Data Scientist leading the development and lifecycle management of models to predict and optimize advertising performance across multiple channels. Responsibilities: define learning objectives, build cross-channel measurement and optimization systems, design causal inference experiments, translate models into actionable marketing recommendations, advance modeling best practices, and collaborate cross-functionally with data, product, and engineering teams from research through deployment. Requirements: 8+ years in data science, machine learning, or statistics; advanced degree in a quantitative field; proficiency in Python and SQL; experience in modeling advertising or marketing performance metrics (e.g., ROAS, CAC); expertise in causal inference, experimentation, and data analysis. Preferred: startup experience building predictive marketing products, familiarity with advertising platform APIs (Meta, Google, TikTok), decision systems such as bidding or recommendation engines, and applying foundation models like LLMs. High-Value: applied in a fast-paced, impact-driven environment; focused on advertising optimization across paid social, search, display, and video channels; experience with data-sparse environments and balancing model sophistication with speed and interpretability. Collaboration: partners across data, product, engineering, and customer discovery teams. Location: remote; no travel specified.