Creating the Fundraising Jobs Dataset (round 1)

Author

Greg Brooks

Published

October 13, 2025

Overview

Perhaps this has already been done - but I’ve been working on creating a fundraising job title and job description dataset. Right now it is scattered, has some irrelevant data, across a few in-progress attempts, which I’ll list below. I have a very small private dataset of recent job titles / descriptions, but the most comprehensive info exists within a recently published public dataset called CMAP.

CMAP Prospect Research Subset

General Statistics

  • Total Records: 430
  • Unique Job Titles: 113
  • Unique Sectors: 22

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CMAP 2017

Explainer

I’ve attempted to subset Prospect Development and Major Gift Officer job titles from the 2025 Career Map (CMap) dataset released in July 2025 (citation below) from the CMAP readme.

Quoting from the paper:

“We generated our dataset by utilizing a collection of 220 million anonymized and publicly available user curriculum vitae (CVs), collected from LinkedIn via DataHut24. These CVs encompass a total of 546 million job experiences spanning 197 countries and 24 industry sectors. While the dataset itself was collected in 2017, the career histories recorded within these CVs extend as far back as 1970, capturing job trajectories of individuals whose professional experiences span multiple decades, up to December 2017.

However, given the noisy nature of the data, substantial pre-processing was required to ensure consistency and usability. The job titles appeared in a variety of formats, often containing spelling variations, abbreviations, or redundant information. Furthermore, sector classifications were inferred rather than explicitly provided, requiring additional processing to associate each job experience with a standardized industry category This repository provides a comprehensive and scalable dataset for analyzing job titles, promotions, and sector specialization across 24 major industry sectors and 197 countries. It is designed for research in career mobility, labor economics, workforce analytics, and computational social science.”

Tangent: You’ll notice that “Prospect Researcher” is the most common job title, by far, for that niche, and ends up becoming their standardized term for that role at a junior level. However, as soon as you add the word “analyst” to the title (e.g. Senior Prospect Research Analyst), that word becomes the standard - so Senior Prospect Research Analysts become standardized into Senior Analysts. This was useful for their analysis, but to properly understand how many “Prospect Researchers” there are - those aggregates will have to be recalculated using a more ecclectic definition. However, given the massive overlap between these niche Prospect Dev roles, perhaps the best aggregate is actually “Prospect Development Professional” to get more accurate totals from the dataset.

Drawbacks to my analysis so far

  • There are some major caveats to both my datasets created: the Prospect Development job title one, and the Major Gift Officer one. Namely, I was struggling to filter the specific titles that match both of those niche’s, something especially difficult given the variety of titles especially for Major Gift Officers.
  • I have included the PowerQuery M code in this repository, in case folks want to see my clumsy attempts to subset CMAP.
  • I created a smaller dataset for the MGO niche, that only includes the non-profit sector. If you sort by frequency, you’ll see familiar titles at the top. The “title_generalized” column is arguably the most helpful, because it attempts to standardize the highly variable development officer roles into several categories.
  • In my dataset filenames, I have been calling this CMAP 2017, to remind me that this is somewhat old data. But because of how comprehensive this data is, and because the methods and code are open-source, future dataset creation could follow in the same vein: the main limiting factor being the cost of purchasing public and anonymized CV data. There may be more recent public sector-specific datasets out there that could be appended to this one, or transformed to match their unique standardization system, and potentially avoid this cost.
  • I have not uploaded the sector files or massive original dataset, to save on repo space, but they can be downloaded here.

Further resources

Direct links, if you want to download my filtered data from GitHub as .csv: - Prospect Development job titles - Major Gift Officer nonprofit sector titles - Major Gift Officer all sector titles - full of irrelevant titles right now, until I filter it better

References

  • Citation: Subhani, S., Memon, S.A. & AlShebli, B. CMap: a database for mapping job titles, sector specialization, and promotions across 24 sectors. Sci Data 12, 1214 (2025). https://doi.org/10.1038/s41597-025-05526-3
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