Senior Manager of Marketing Experimentation, Spreetail

• If you are looking for a career-defining role and an opportunity to learn from the best, this role is for you
• You are an A/B/n testing ninja and can run MDEs and pre/post-test analyses in your sleep
• You understand how to systematically drive a learning roadmap from hypothesis generation, to selecting the best experiment designs, to delivering a causal learning run to the MDE you designed for pre-test
• You have run hundreds of randomized controlled experiments, delivered 100%+ gains on your primary KPIs, and operationalized your learnings in org-wide rollouts to capture the full value
• You love statistics and data science and understand signal, noise, power, and alternative test designs to A/B/n when you are in non-ideal learning environments
• You can say no, manage up, and communicate to focus your learning roadmap on what is best for our brands and company
• You understand how to design experiments that test one data model or technology vs another
• You can break down major initiatives and insights needs into smaller components and tasks
• Advanced understanding of experimentation designs and measurement capabilities and requirements to drive successful learnings
• Hands-on experience building and deploying end-to-end experimentation programs at scale
• Excellent communication abilities
• Excellent project management capabilities

Desirable

• We are looking for a Senior Manager of Marketing Experimentation to own and operate our advanced and proprietary experimentation engines, roadmaps, and methodologies for advertising and content
• This role will report directly to our Chief Marketing Officer and will be the SME for marketing experimentation across our company
• You will work with a talented team of executives and experts, including those who have run advanced experimentation at Amazon, Microsoft, and startups
• This team and leader are responsible for some of the most critical learnings at Spreetail and take pride in being the first to solve many practical causal challenges at scale