Marketing Analytics Lead designing and implementing a spend optimization model at Caesars Entertainment. Collaborating across teams to drive marketing investment decisions with data-driven insights.
Responsibilities
Design and implement a daily-refreshing spend allocation model that integrates performance data, incrementality signals, and budget constraints.
Develop logic to recommend optimal channel- and sub-channel-level spend based on performance, saturation curves, marginal ROI, and forecasted outcomes.
Create dashboards, APIs, or automated reports that surface recommendations to the marketing organization in a clear, actionable format.
Build and maintain data pipelines that ingest data from ad platforms, MMPs, internal databases, and attribution systems.
Ensure data quality, freshness, and model reliability through automated checks and monitoring systems.
Develop or refine Media Mix Models (MMM), incrementality frameworks, marginal return curves, and/or bid/optimization simulations.
Partner with finance, marketing, and product to integrate non-marketing factors (seasonality, product promos, inventory, etc.) into spend recommendations.
Serve as the analytical thought leader for marketing investment decisions.
Work closely with channel owners to ensure recommendations align with campaign strategy and creative cycles.
Collaborate with engineering to productionize models and maintain scalable infrastructure.
Lead A/B tests and incrementality experiments to improve the accuracy of spend recommendations.
Iterate on the model as new channels, geos, or strategies come online.
Requirements
Must be 21 years of age or older
5+ years in marketing analytics, data science, growth analytics, or similar.
Strong proficiency in Python or R, SQL, and scalable model deployment workflows.
Experience building MMM, incrementality tests, or ROI/attribution models.
Knowledge of paid media channels (paid social, search, affiliate, programmatic, etc.) and their underlying mechanics.
Experience with visualization tools (Tableau, Power BI, Looker) for delivering insights and automated reporting.
Ability to turn complex modeling outputs into simple business recommendations.
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