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Remote mid-level data scientist title but niche MMM/Bayesian specialization moderates applicant competition.
Strongly marketing-focused MMM and agency experience make background transferability limited across domains.
Explicit 3–6 years plus mandatory MMM, Bayesian, causal inference, SQL and Power BI requirements.
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Develop and optimize Marketing Mix Models (MMM) and Bayesian statistical models to measure marketing impact and support budget allocation.
Apply causal inference methodologies and advanced statistical modeling (regression, hierarchical Bayesian, time-series) to analyze marketing effectiveness and incrementality.
Build scalable Python analytics pipelines and automated dashboards; collaborate cross-functionally to translate analytics into marketing optimization strategies.
3–6 years experience in Marketing Analytics, Marketing Science, Applied Data Science, Econometrics, or Media Analytics.
Expert knowledge of Marketing Mix Modeling (MMM) and strong understanding of Bayesian inference and causal inference techniques.
Proficient in Python (pandas, NumPy, SciPy, scikit-learn, PyMC/PyMC3, Statsmodels) and SQL.
Experience with Power BI or Looker Studio for dashboarding and reporting.
Experienced in agency, consulting, or digital marketing analytics environments, with hands-on marketing science application skills.
Strong statistical modeling background including linear, multivariate, hierarchical Bayesian, and econometric modeling.
Able to build and deploy scalable Python-based analytics solutions and communicate technical findings to business stakeholders effectively.