Background information about the job or company (e.g., role context, company overview)

GiveDirectly is the first - and largest - nonprofit that lets donors like you send money directly to the world’s poorest. We believe people living in poverty deserve the dignity to choose for themselves how best to improve their lives - cash enables that choice.

Responsibilities or duties

Data Product and Metrics Ownership

• Set vision and priorities for how data supports fundraising and marketing strategy, based on a deep understanding of GiveDirectly’s fundraising model, donor lifecycle, and growth strategy, across revenue streams
• Define, standardize, and document core fundraising and marketing KPIs (e.g., retention, LTV, revenue forecasting metrics) to drive fundraising goals
• Design and build robust data models in the warehouse that serve as the source of truth for fundraising reporting - weekly, monthly, quarterly and annually
• Establish clear, trusted source-of-truth datasets and metric logic used consistently across Fundraising, Finance, and Leadership, continually folding learnings in to update the metrics and definitions
• Identify recurring analytical needs and convert them into scalable, automated data products
• Scope and prioritize fundraising data products in partnership with Fundraising and Marketing Data stakeholders
• Partner with data engineering on upstream data quality and pipeline improvements, while owning the downstream analytical layer
• Continuously reduce ad hoc reporting by investing in durable systems that compound in value over time

Strategic Fundraising and Marketing Analytics

• Serve as the primary data partner to fundraising and marketing leadership, informing strategy across acquisition, retention, and revenue expansion
• Build and maintain revenue forecasts and donor cohort models to guide annual planning and budget allocation
• Support channel investment decisions with clear ROI frameworks, experiment design, and performance analysis
• Proactively surface decision-relevant insights, even when questions are not fully formed

Donor & Revenue Insights

• Conduct deep-dive analyses into donor cohorts, retention drivers, churn patterns, and cost per dollar raised to drive continuous improvement on fundraising performance
• Evaluate marketing and fundraising initiatives using appropriate analytical methods (including experimentation where feasible, though not all channels support large-scale testing)
• Translate complex analyses into clear recommendations for non-technical stakeholders that drive fundraising improvements
• Surface high-leverage opportunities to improve conversion, retention, and revenue durability

Data Infrastructure & Platform Contribution

• Collaborate with data engineers and analytics peers to improve data quality, access, and trust
• Partner with the Salesforce Lead to ensure CRM data structure and integrity support accurate reporting and scalable downstream modeling
• Contribute to the long-term roadmap for fundraising data architecture, ensuring systems are designed for scalability and compounding insight generation over time
• Contribute to improvements in GiveDirectly’s data capabilities such as adopting conversational analytics
• Maintain high standards of analytical rigor, documentation, and reproducibility

Qualifications or requirements (e.g., education, skills)

We recognize that strong candidates may not meet every requirement listed below. If this role excites you, we encourage you to apply.

Required

• 8+ years of experience in analytics, data science, or quantitative strategy roles, including a track record of driving marketing, growth, or fundraising performance through analytics
• Strong experience setting vision and priorities for data products from significant ambiguity
• Strong systems thinking and experience designing measurement systems that compound in value and reduce recurring friction
• Advanced SQL skills and demonstrated experience building production-grade data models in a data warehouse
• Strong Python proficiency
• Experience building forecasting models (e.g., revenue, LTV, retention)
• Experimentation and performance measurement experience (e.g., A/B testing, quasi-experimental methods, ROI evaluation)
• Ability to navigate ambiguity and independently shape how data informs organizational strategy

• Set vision and priorities for how data supports fundraising and marketing strategy, based on a deep understanding of GiveDirectly’s fundraising model, donor lifecycle, and growth strategy, across revenue streams
• Define, standardize, and document core fundraising and marketing KPIs (e.g., retention, LTV, revenue forecasting metrics) to drive fundraising goals
• Design and build robust data models in the warehouse that serve as the source of truth for fundraising reporting - weekly, monthly, quarterly and annually
• Establish clear, trusted source-of-truth datasets and metric logic used consistently across Fundraising, Finance, and Leadership, continually folding learnings in to update the metrics and definitions
• Identify recurring analytical needs and convert them into scalable, automated data products
• Scope and prioritize fundraising data products in partnership with Fundraising and Marketing Data stakeholders
• Partner with data engineering on upstream data quality and pipeline improvements, while owning the downstream analytical layer
• Continuously reduce ad hoc reporting by investing in durable systems that compound in value over time
• Serve as the primary data partner to fundraising and marketing leadership, informing strategy across acquisition, retention, and revenue expansion
• Build and maintain revenue forecasts and donor cohort models to guide annual planning and budget allocation
• Support channel investment decisions with clear ROI frameworks, experiment design, and performance analysis
• Proactively surface decision-relevant insights, even when questions are not fully formed
• Conduct deep-dive analyses into donor cohorts, retention drivers, churn patterns, and cost per dollar raised to drive continuous improvement on fundraising performance
• Evaluate marketing and fundraising initiatives using appropriate analytical methods (including experimentation where feasible, though not all channels support large-scale testing)
• Translate complex analyses into clear recommendations for non-technical stakeholders that drive fundraising improvements
• Surface high-leverage opportunities to improve conversion, retention, and revenue durability
• Collaborate with data engineers and analytics peers to improve data quality, access, and trust
• Partner with the Salesforce Lead to ensure CRM data structure and integrity support accurate reporting and scalable downstream modeling
• Contribute to the long-term roadmap for fundraising data architecture, ensuring systems are designed for scalability and compounding insight generation over time
• Contribute to improvements in GiveDirectly’s data capabilities such as adopting conversational analytics
• Maintain high standards of analytical rigor, documentation, and reproducibility

• Advanced SQL skills
• Strong Python proficiency
• Forecasting models (e.g., revenue, LTV, retention)
• Experimentation and performance measurement (e.g., A/B testing, quasi-experimental methods, ROI evaluation)
• Systems thinking
• Data product development
• Analytical rigor
• Documentation
• Reproducibility

• 8+ years of experience in analytics, data science, or quantitative strategy roles, including a track record of driving marketing, growth, or fundraising performance through analytics
• Strong experience setting vision and priorities for data products from significant ambiguity
• Strong systems thinking and experience designing measurement systems that compound in value and reduce recurring friction
• Advanced SQL skills and demonstrated experience building production-grade data models in a data warehouse
• Strong Python proficiency
• Experience building forecasting models (e.g., revenue, LTV, retention)
• Experimentation and performance measurement experience (e.g., A/B testing, quasi-experimental methods, ROI evaluation)
• Ability to navigate ambiguity and independently shape how data informs organizational strategy