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Specialized forecasting/causal/optimization role at a non-tech publisher reduces applicant density.
Advanced forecasting, causal and optimization skills are transferable across retail and media, but industry knowledge preferred.
Requires 6+ years plus forecasting, causal inference, optimization, production deployment, and tooling experience.
Convert ambiguous business questions into clear decision problems and analytical plans focused on marketing effectiveness, demand forecasting, pricing, and supply-chain optimization.
Develop and maintain advanced forecasting models (time-series, probabilistic, machine-learning) and optimization solutions for pricing, inventory, and resource allocation with measurable business impact.
Build, deploy, monitor, and continuously improve decision models and AI-assisted tools to drive adoption and operationalize data-driven decisions across teams.
6+ years of experience in decision science, data science, applied economics, operations research, forecasting, or quantitative analytics.
Strong expertise in Python and SQL with advanced time-series modeling, probabilistic and hierarchical forecasting skills.
Experience with causal inference methods for marketing impact measurement and mathematical optimization in pricing or supply-chain contexts.
Proven end-to-end production experience moving models from development to production environments using modern software practices and cloud deployment.
Experienced in driving business outcomes by translating model outputs to actionable decisions with skillful communication of uncertainty and tradeoffs.
Comfortable working with responsible AI and generative AI tools to accelerate analytics workflow and solution delivery.
Background or interest in publishing, media, retail, or consumer products with familiarity in modern solution patterns like orchestration, automated retraining, and decision-support applications.