Responsibilities

• Execute platform-level Conversion Lift Studies (CLS) and Brand Lift Studies (BLS) to quantify the incremental impact of mid-funnel campaigns on qualified traffic, intent lift, and bookings.
• Partner with data science to initiate and analyze geo-randomized tests that isolate mid-funnel contribution from organic and other paid channels, translating results into clear investment recommendations.
• Own mid-funnel measurement and reporting — build and maintain dashboards tracking primary KPIs (intent/preference lift, qualified traffic, incremental bookings, cost per booking) and creative performance across asset clusters.
• Analyze creative and audience performance to determine which messages, formats, and audience segments most efficiently drive consideration and downstream conversion.
• Surface proactive insights on budget allocation, audience saturation, creative fatigue, and cross-funnel lift to inform regular optimization cycles and strategic recommendations for senior marketing leaders.
• Contribute to the learning agenda by structuring test-and-learn plans that answer key strategic questions (e.g., does mid-funnel investment reduce blended CPA? which reasons-to-believe drive the highest intent lift?).
• Collaborate cross-functionally with paid social media on campaign execution, integrated marketing on messaging strategy, and agency partners on creative performance feedback loops.

Requirements

• 7+ years in analytics or data science, with depth in marketing/lifecycle measurement, CRM or customer outreach, or growth analytics.
• Strong experimentation and causal inference skills, with experience designing, executing, and analyzing experiments.
• Measurement fluency — hands-on experience with campaign/lifecycle measurement across channels (email, push, and emerging channels), attribution methodologies, and their limitations.
• A track record of analysis that drives action, directly shaping lever prioritization, channel strategy, and resourcing decisions, with influence through the Director level.
• Excellent communication skills, able to distill complex measurement results into clear, compelling recommendations for senior stakeholders — and comfortable pushing back when the data warrants it.
• Technical proficiency in advanced SQL and Python/R, with experience in large-scale data environments and building scalable reporting.
• An AI-forward mindset, with experience leveraging AI tools to automate workflows, accelerate analysis, and build scalable solutions.
• Quantitative degree required; advanced degree preferred.

Preferred

• An AI-forward mindset, with experience leveraging AI tools to automate workflows, accelerate analysis, and build scalable solutions.
• Advanced degree preferred.