Senior Marketing Mix Modeling Data Scientist (Remote)

Design, develop, and refine end-to-end Marketing Mix Models using the Meridian framework to quantify the incremental impact of offline and online media channels
Build and maintain scalable Python pipelines for data ingestion, feature engineering (addressing seasonality, price elasticity, and macro-trends), and automated model validation
Utilize Bayesian methods to incorporate prior knowledge and reach deeper insights into diminishing returns (S-curves) and media ad-stock effects
Translate technical model outputs (ROI, mCPA, and contribution charts) into executive-level narratives that guide CMOs and media planners in budget reallocation
Partner with the broader Data Science team to calibrate MMM results using "Ground Truth" data from incrementality tests (Geo-experiments or Lift studies)

Proven track record of deploying Google’s Meridian (or similar Bayesian frameworks like LightweightMMM) in a production environment
Advanced proficiency in Python, specifically within the data stack (Pandas, NumPy, PyMC/ArviZ, and Scikit-learn)
6+ years of experience in econometrics or causal inference, with a deep understanding of time-series analysis and multivariate regression
A solid grasp of the modern media landscape, including nuances in programmatic, social, search, and traditional broadcast attribution
Very good English skills

Experience scaling workflows on Google Cloud Platform (GCP), specifically using BigQuery for data warehousing and Vertex AI for model lifecycle management
Familiarity with CI/CD, Git workflows, and containerization (Docker) to ensure model reproducibility
Ability to build custom dashboards (Looker, Tableau, or Streamlit) to make model results accessible to non-technical stakeholders

Senior Marketing Mix Modeling Data Scientist