Principal Data Scientist

8+ years in data science or ML engineering roles. PhD (or equivalent experience) in statistics, econometrics, machine learning, or a related quantitative field. Strong foundation in causal inference, experimental design, and Bayesian modeling. Proven experience applying statistical methods to marketing measurement, attribution modeling, and related incrementality/experiment design problems. Experience in building or optimizing large-scale A/B testing or experimentation infrastructure. Experience designing, deploying, and operating statistical/ML models that drive real business decisions at scale. Proficiency in Python/R/SQL and modern data platforms (BigQuery, Snowflake). Familiarity with MLOps practices and data pipeline orchestration tools. Strong communication skills.

Own and advance the statistical foundations of marketing-effectiveness measurement. Research, prototype, and deploy advanced attribution and AI models for marketing measurement. Design and evolve complex data science models that balance scientific rigor with practical, scalable frameworks. Build and maintain experimentation frameworks for model validation and lift measurement. Define and implement validation and release criteria for attribution model and product launches. Collaborate with Software Engineers and Product Managers to productionize and productize your work. Mentor Data Scientists on complex statistical and methodological challenges.