Research Manager

Manage market research projects, oversee client relationships, and ensure high-quality data collection. Coordinate with internal teams and external partners.

This Senior User Researcher role shapes Snowflake's product strategy by leading qualitative and quantitative research to inform product and go-to-market decisions. The role involves partnering with product, design, engineering, and marketing leads, and using customer research, product usage data, and AI applications to identify opportunities and drive outcomes. The ideal candidate has 5+ years of user research experience, deep expertise in mixed methods, and a track record of influencing decisions.

The Research Manager will lead and manage research projects, likely overseeing data collection, analysis, and reporting to deliver actionable insights. The role requires strong project management and analytical skills to support business decisions.

Coordinates research initiatives, manages timelines, and facilitates cross-functional collaboration for research projects. Requires strong project management and communication skills.

Defines and ships new AI-native products and workflow experiences for research operations at Metaforms. Conducts user discovery with research operators and agency leaders, translates messy qualitative input into product insights, and partners with engineering through delivery. This is a high-ownership 0-to-1 role requiring first-principles reasoning in ambiguous spaces.

Owns the onboarding process for new research agency customers at Metaforms, from signed contract to fully live on the platform. Builds structured onboarding playbooks, runs kickoff calls and training sessions, and manages pilots with new accounts to drive time-to-value. Collaborates with Sales, Product, and Technical Support to resolve blockers and feed customer feedback back into the platform.

Owns the design, development, and continuous improvement of AI agent systems that power modern research operations at Metaforms. Responsibilities include building the agent harness, driving long-context handling and cascading-error mitigation, defining evaluation rubrics, and building production monitoring and eval suites for high-stakes outputs. Requires experience building and operating agentic systems in production with multi-step pipelines and tool use.