Givzey

Givzey

Director of Fundraising Intelligence

United States · Director

Sponsorship not specifiedDetected 35 days ago
PythonSQLMachine LearningData AnalysisData ScienceData VisualizationExcelCustomer SuccessCadenceResearchLeadershipCommunicationPublic Speaking

About the role

  • Version2.ai's Virtual Engagement Officer is deployed across 200+ fundraising organizations - universities, nonprofits, healthcare foundations.

Responsibilities

  • We're creating a new role - Director of Fundraising Intelligence - to own that market data from analysis to publication.
  • This critical role will build on Version2.ai's reputation as the most credible, data-driven voice in autonomous fundraising.
  • What You'll Own Collective Insights (External/Marketing) Identify cross-cutting insights about autonomous AI in fundraising and partner closely with our marketing team to share with our audience.
  • Develop original research: benchmarks, trend reports, sector comparisons, giving behavior analysis An example of the output we're after: we know that one-third of donors who give through the VEO increase their gift by 65%.

Requirements

  • You are the function.

Nice to have

  • If you're most comfortable behind a screen and the idea of a conference keynote sounds like a nightmare, this isn't the right fit.

Skills

  • About Givzey / Version2.ai Join the Future of Fundraising at Givzey!
  • It has over 1M activities, 100k engagement, and is approaching $20M in gifts.
  • With these results come gift size changes, lapsed donor recovery, cadence performance, segment behavior, donor renewal rates.
  • Half the job is the work; the other half is standing in front of a room and delivering it.
  • Who You'll Work With You report to the

Company info

  • This person will conduct meaningful market analysis, arm our Sales and Customer Success teams with statistical firepower, and add value for our customers by providing deeper insights into benchmarks for success.

This listing is sourced directly from Givzey's careers page and normalized into a canonical job model.