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Remote and mid-level experience increase applicant pool, but niche Bayesian MMM skills moderate competition.
Highly domain-specific marketing analytics and MMM expertise reduces cross-industry transferability.
Explicit 3–6 years plus specialized Bayesian MMM, causal inference, and required tech stack drive strict filtering.
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Develop and optimize Marketing Mix Models (MMM) including Bayesian statistical models to measure marketing investment impact and support budget allocation.
Apply causal inference techniques and advanced statistical modeling (regression, hierarchical Bayesian, time-series) to analyze marketing effectiveness and forecast outcomes.
Build scalable Python analytics pipelines and automated dashboards (Power BI or Looker Studio); communicate insights and model limitations to stakeholders.
3–6 years experience in Marketing Analytics, Marketing Science, Applied Data Science, Econometrics, or Media Analytics.
Expert knowledge of Marketing Mix Modelling (MMM) and Bayesian inference/statistical techniques.
Strong Python programming skills with relevant libraries (pandas, NumPy, SciPy, scikit-learn, PyMC/PyMC3, Statsmodels) and SQL proficiency.
Experience with Power BI or Looker Studio for dashboard/reporting development.
Experience working in agency, consulting, or digital marketing analytics environments, especially with MMM frameworks like Google Meridian.
Demonstrated ability to apply causal inference methodologies (Difference-in-Differences, Synthetic Control, Propensity Score Matching, Instrumental Variables, Uplift Modelling).
Operates at a senior analytical level capable of independently building complex statistical models and delivering actionable marketing insights across cross-functional teams.