Analyst II, Paid Media Experimentation

Squarespace•New York, NY

11d•Hybrid

About The Position

Squarespace is looking for an Analyst to grow experimentation for our paid media program. You will power our marketing investment strategy and optimization by running experiments, creating processes and plans and implementing tooling to promote a test-and-learn culture and ensure consistency, thoughtfulness and scalability across teams. You have hands-on experience framing hypotheses and outcomes, designing and analyzing different types of experiments, making clear recommendations and creating repeatable processes for direct response and brand channels. This is a hybrid role working from our New York City headquarters (3 days per week). You will report to the Manager of our Marketing Services Analytics & Operations team.

Requirements

3+ years of experience in a marketing analytics or data science role
Hands-on experience designing, implementing and analyzing experiments across paid media channels (e.g., search, social, programmatic, and brand)
Foundation in statistical methods such A/B testing, causal inference and variance analysis
Experience building repeatable experimentation processes through documentation, frameworks or tooling
Proficiency in SQL for querying large datasets and Python or R for analysis; familiarity with dbt
Experience creating dashboards in tools like Looker or Tableau
Translate complex findings into clear, actionable recommendations for technical and non-technical audiences
Passion for experimentation, media measurement and continuous learning

Responsibilities

Partner with in-house Marketing teams to translate business questions into clear hypotheses and measurable outcomes
Develop several types of experiments across direct response and brand media channels to measure incrementality and optimize performance
Analyze results and translate into to relevant recommendations
Build frameworks, plans, and dashboards to grow experimentation and reporting consistency across channels and teams.
Partner with Data Science, Engineering and other Analysts to develop repeatable frameworks, processes and tooling to grow testing across channels
Monitor latest industry best practices and tools to refine our approach
Socialize approaches and insights to promote a test-and-learn culture