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Metro mid-level role with niche Bayesian MMM skills reduces applicant density despite brand and location.
Highly domain-specific Bayesian MMM, causal inference, and optimization skills limit cross-industry transferability.
Explicit 5–10 years plus specialized Bayesian, Stan/PyMC and optimization requirements.
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Design, develop, and deploy advanced Bayesian Marketing Mix Models to optimize marketing investments and improve media effectiveness.
Build probabilistic and hierarchical Bayesian models using PyMC and/or Stan, incorporating causal inference and multi-stage modeling frameworks.
Develop and implement optimization frameworks for marketing budget allocation balancing ROI and business constraints, collaborating closely with cross-functional teams.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Economics, Operations Research, or related quantitative discipline.
5–10 years of experience in Data Science, Statistical Modeling, or Marketing Analytics.
Strong Python programming skills with experience in Pandas, NumPy, Scikit-learn, SciPy, and SQL (including complex joins and performance optimization).
Proven experience building production-grade Bayesian statistical models including use of PyMC or Stan and implementing marketing mix modeling techniques such as adstock transformations and saturation curves.
Expertise in Bayesian statistics and probabilistic programming with hands-on experience in MCMC sampling, HMC, NUTS, and Bayesian diagnostics.
Experience applying advanced causal inference methods (e.g., geo experiments, Difference-in-Differences, Synthetic Control) and optimization techniques in marketing analytics context.
Ability to deliver scalable, production-quality analytics solutions and communicate complex statistical concepts effectively to technical and business stakeholders.