Temporary Data Analyst for Donation Analysis

We are looking for a temporary data analyst or data scientist to support a time-bound internal analysis of donation data for an external-facing partner project. This is not an open-ended research role. We already have a defined analytic direction and specific questions we need answered. We need someone who can work carefully through prescribed analysis paths, produce clean summaries, validate their work, and help translate results into high-quality external-facing deliverables in collaboration with our marketing and creative teams. Project overview The project involves analyzing internal donation behavior data to support a partner-facing report, presentation, or other external-facing product deliverable. The right person will help us examine donor and organization-level patterns, summarize key metrics, and produce reliable outputs that can be reviewed internally and then adapted into polished materials for external partners and audiences. The work may include analyses such as: Donor behavior over time Giving frequency and consistency Pre/post comparisons Group comparisons Descriptive statistics and distribution summaries Segmentation by donor, organization, time period, or other predefined characteristics Simple statistical testing where appropriate Creation of tables and charts for internal review and external-facing use Documentation of methods, assumptions, filters, and limitations Collaboration with marketing and creative partners to ensure analytic outputs are accurate, clear, and usable in polished deliverables We will provide the analytic questions, business context, and preferred definitions. We are looking for someone who can execute carefully, ask good clarification questions, flag data quality or interpretation issues, and work well with both technical and non-technical collaborators. Required skills Strong SQL Strong analytical judgment with real-world business or nonprofit data Experience with Python or R for data analysis Ability to work with transaction-level data Careful attention to joins, denominators, duplicate records, missingness, and time-window definitions Ability to create clean tables and simple charts Ability to explain findings clearly without overstating conclusions Comfort working from a defined analysis plan rather than inventing a broad research agenda Ability to collaborate with non-technical stakeholders, including marketing, creative, communications, or partner-facing teams Preferred skills Experience with donation, nonprofit, fintech, payments, fundraising, subscription, or customer behavior data Experience with donor, user, or account-level behavioral analysis Experience with pre/post analysis, cohort analysis, segmentation, or retention-style metrics Experience preparing analysis for external reports, partner deliverables, executives, public-facing materials, or thought-leadership content Experience working with designers, marketers, writers, or creative teams to turn analysis into clear charts, claims, narratives, and visual assets Familiarity with Databricks, Snowflake, BigQuery, dbt, or similar modern data environments Experience with causal inference or quasi-experimental analysis is a plus, but not required unless the project scope expands What we are not looking for We are not looking for someone to “find insights” in a vague or exploratory way. We are also not looking for someone who only makes dashboards without understanding the underlying data logic. We need someone who is careful, methodical, collaborative, and able to produce defensible analysis from specified questions and definitions. The work must be accurate enough to support external-facing partner deliverables, so attention to detail and responsible interpretation are essential. Expected deliverables Deliverables may include: Reproducible SQL, Python, or R scripts/notebooks Clean summary tables Simple charts suitable for internal review or partner-facing adaptation Short written summaries of findings Plain-English takeaways that can support partner-facing materials Documentation of data filters, definitions, assumptions, and known limitations QA checks showing that key counts and totals were validated Review support for marketing or creative materials to ensure analytic claims, charts, and captions are accurate Ideal working style The ideal person is: - Careful and skeptical with data - Comfortable asking precise clarification questions - Able to work independently once the analysis path is defined - Clear in written communication - Good at documenting their logic - Practical and efficient, not overly academic - Collaborative with marketing, creative, and non-technical stakeholders - Able to distinguish what the data shows from what it does not show - Able to help turn analysis into clear, credible, external-facing outputs without overstating the findings Data confidentiality This project involves sensitive internal donation-related data. The selected contractor must be willing to follow strict confidentiality and data-handling requirements. Data may need to be accessed only through approved systems, and no data may be downloaded, reused, or shared outside the approved project environment.