Media Strategist

Squarespace·New York City·onsite
crypto:applicationbusinessIC4Marketing
Compensation
Not disclosed
Squarespace is hiring a Paid Social Media Strategist to run campaigns across Paid Social platforms including Meta, TikTok, Reddit and Pinterest. This is a hands-on buying role on global, full-funnel campaigns with a clear goal: drive incremental subscriptions, efficiently, at scale. You will join a collaborative, fast-moving team that values curiosity, clear thinking, and a bias toward action. We let data guide decisions, move quickly when we have enough to go on, and push each other to keep digging when we don't. AI is central to how this team operates, but it's a tool and not a replacement for judgment. The people who thrive here know when to lean on it and when to think harder. This is a hybrid role working our New York office 2-3 days per week and reports to the Manager of Paid Social Media. You’ll Get To… Own campaign strategy and execution across Meta, TikTok, Reddit and Pinterest, spanning formats, markets, and the full marketing funnel. Build, launch, and optimize campaigns daily: audiences, creative rotations, bidding strategies, budget pacing, and troubleshooting. Design and run structured tests across creative, audience, and format, and help shape the testing roadmap against cross-functional marketing goals. Use AI to automate reporting, accelerate analysis, and improve the quality and speed. Build repeatable workflows, not one-offs — and bring the judgment to know when the output is right and when it needs a harder look. Pressure-test platform-reported results against incrementality measurement and bring a point of view on what is actually working. Surface insights, keep performance reporting current, and know your numbers cold Manage relationships with platform partners and work cross-functionally with creative, analytics, and finance. Who We’re Looking For 2+ years of hands-on paid social buying for direct response campaigns. Deep Meta expertise is required. Pinterest and Reddit experience is strongly preferred. Experience optimizing lar