Responsibilities

• Investment Decisioning & Data Science / Finance Partnership: Own the operating partnership with Data Science and Finance on investment decisions and channel mix — translating MMM output, incrementality findings, and marketplace signal into a shared point of view on where dollars should go
• Serve as the connective tissue between the quantitative model and the media strategy — ensuring model recommendations account for real-world channel, creative, and seasonal dynamics
• Build and maintain the recurring cadence, documentation, and decision log that governs how investment decisions get made and revisited across the fiscal year
• Partner with Finance on forecast accuracy, in-season reallocation, and the tradeoffs between committed and flexible spend
• Weekly Investment Reviews with team and SLT
• Own and drive the weekly SLT Investment Review — setting the agenda, structuring the narrative, and presenting channel-level and envelope-level performance, risks, and recommendations to senior leadership
• Lead with the so-what: distill dense model output and channel results into a small number of clear, decision-ready recommendations
• Track and communicate key investment metrics (ITPY, mROI, impression share) across channels and escalate risks or opportunities in season
• Build trust with SLT as a credible, consistent voice on where the business stands and what to do about it
• Central Decision Engine & Intelligent Decision Architecture: Partner with the Measurement & Intelligent Decision Architecture team to build the Central Decision Engine — the unified layer that synthesizes MMM, platform data, incrementality, and attribution into a single investment decision source

Requirements

• 7–10 years in paid media investment strategy, marketing analytics, or a related quantitative marketing role, ideally in a high-investment, performance-oriented environment
• Demonstrated experience partnering with Data Science and/or Finance functions on investment or resource allocation decisions
• Direct experience with marketing mix modeling (MMM) — and comfort translating model output into a business recommendation
• Track record of owning and presenting to senior leadership on a recurring basis; strong executive presence and the ability to defend a recommendation under questioning
• Working knowledge of an integrated media environment: search, programmatic/digital, social, video/streaming, and above-the-line, and how they interact
• Experience managing or directly influencing a large media investment envelope, including forecasting and in-season reallocation
• Comfort working with data infrastructure teams (Databricks or equivalent) to define architecture requirements, even without being a hands-on engineer
• Strong prioritization and organization skills necessary to lead cross-functional and cross-agency initiatives in a highly matrixed, fast-moving environment
• BA/BS degree required
• Minimal travel for industry networking, media, and related events