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Niche MMM/Bayesian skills reduce applicant pool despite a mid-level Data Scientist title and mid experience band.
Marketing-focused MMM and incrementality expertise limits transferability to non-marketing domains.
Explicit 3–6 years plus mandatory MMM, Bayesian, and causal inference skills enforce strict screening criteria.
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Develop and optimize Marketing Mix Models (MMM) to measure multi-channel marketing impact and support budget allocation.
Construct Bayesian statistical models for marketing effectiveness, forecasting, causal inference, and scenario planning.
Build Python-based analytics pipelines, dashboards, and reports to translate statistical findings into actionable marketing strategies.
3–6 years experience in Marketing Analytics, Marketing Science, Applied Data Science, Econometrics, or Media Analytics.
Expertise in Marketing Mix Modelling (MMM) and strong knowledge of Bayesian inference and statistical modeling techniques (linear/multivariate regression, hierarchical models, time-series, econometrics).
Hands-on experience with causal inference methodologies such as difference-in-differences, synthetic control, propensity score matching, instrumental variables, and uplift modeling.
Strong proficiency in Python (pandas, NumPy, SciPy, scikit-learn, PyMC/PyMC3, Statsmodels) and SQL; experience with Power BI or Looker Studio.
Experienced in agency, consulting, or digital marketing analytics environments with deep domain expertise in MMM and Bayesian methods.
Skilled in applying advanced causal inference and econometric techniques for marketing impact measurement and incrementality testing.
Capable of building scalable, automated analytics pipelines and dashboards integrating complex statistical results for business decision-making.