Civil Service Statistics data browser (2026)

Data preview: All civil servants / Sex / Parent_department / Profession_of_post

Explore further: Organisation, Responsibility_level_grouped, Responsibility_level_ungrouped, Region_london, Region_ITL1, Region_ITL2, Region_ITL3, Function_of_post, Ethnicity, Disability, Sexual_orientation, Age

Status	Year	Sex	Parent_department	Profession_of_post	Headcount	FTE	Mean_salary	Median_salary
In post	2026	Female	Attorney General’s Departments	Commercial	35	35	48450	44020
In post	2026	Female	Attorney General’s Departments	Communications	70	65	47510	45020
In post	2026	Female	Attorney General’s Departments	Cyber	[c]	[c]	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Digital and Data	95	95	49020	44730
In post	2026	Female	Attorney General’s Departments	Finance	70	70	46100	38940
In post	2026	Female	Attorney General’s Departments	Human Resources	225	220	44370	38000
In post	2026	Female	Attorney General’s Departments	Knowledge and Information Management	55	50	43850	34650
In post	2026	Female	Attorney General’s Departments	Legal	4290	3935	65250	63500
In post	2026	Female	Attorney General’s Departments	Operational Delivery	3010	2780	32290	32110
In post	2026	Female	Attorney General’s Departments	Other	15	15	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Policy	95	90	55640	47350
In post	2026	Female	Attorney General’s Departments	Project Delivery	105	100	48780	44340
In post	2026	Female	Attorney General’s Departments	Property	15	15	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Risk Management	[c]	[c]	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Security	30	25	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Social Research	10	10	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Statistics	[c]	[c]	[c]	[c]
In post	2026	Female	Attorney General’s Departments	Unknown	[c]	[c]	[c]	[c]
In post	2026	Female	Cabinet Office	Clinical	5	5	[c]	[c]
In post	2026	Female	Cabinet Office	Commercial	1150	1120	68460	65050
Note:
Data has been truncated to 20 rows, please download the data to view the remaining rows

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About: The Civil Service Statistics data browser is a pilot project by Cabinet Office to provide access to more detailed data on the Civil Service workforce from the Annual Civil Service Employment Survey. We welcome feedback or comments on this project, which can be addressed to civilservicestatistics@cabinetoffice.gov.uk

Notes: Summary figures are suppressed when information relates to less than 5 civil servants for FTE or Headcount, and less than 10 civil servants for median and mean salary (shown as [c]). Zero responses and salaries for less than 30 civil servants have been suppressed for GPDR special category data. FTE figures are not shown for entrants or leavers due to data quality concerns for these groups. Figures are rounded to the nearest 5, or £10 as appropriate.

Data source: All figures are aggregated from the Cabinet Office Annual Civil Service Employment Survey collection.

Version: Generated on 2026-07-16

Data column	Description
Status	Employment status of the civil servants.
	In post - includes staff that were in post on the reference date (31 March).
	New entrant CS - includes new entrants to the Civil Service over the year (1 April to 31 March).
	Leaver CS - includes leavers from the Civil Service over the year (1 April to 31 March). This includes employees who have an Unknown leaving cause.
	Leaver Dept. - includes leavers from the department over the year (1 April to 31 March), who did not leave the Civil Service.
Year	Year of data collection (as at 31 March).
Parent_department	Government Department, total figures for both Ministerial and Non-Ministerial Departments include all of their Executive Agencies.
Profession_of_post	Professions relate to the post occupied by the person and are not dependent on qualifications the individual may have.
	Three bodies did not report any professions information for their employees. These are as follows: Scottish Forestry, Forestry and Land Scotland and Serious Fraud Office.
	The Serious Fraud Office have not reported any data for profession, function, ethnicity, disabilty and sexual orientation due to alignment concerns with these fields with external reporting criteria. The Serious Fraud Office is currently undertaking work to ensure a robust dataset is reported for the 2027 ACSES return.
Sex	Self reported sex.
	"Unknown" accounts for employees who were recorded with an unknown sex.
	Figures on sex presented here are based on management information provided by departments, which records the ‘legal sex’ of civil servants. ‘Legal sex’ does not align with ‘sex’ as defined by The Equality Act 2010.
	The Serious Fraud Office have not reported any data for profession, function, ethnicity, disabilty and sexual orientation due to alignment concerns with these fields with external reporting criteria. The Serious Fraud Office is currently undertaking work to ensure a robust dataset is reported for the 2027 ACSES return.
Headcount	Total number of civil servants (rounded to nearest 5).
FTE	Total full-time equivalent (FTE) employment numbers (rounded to nearest 5).
	FTE figures are not shown for entrants or leavers due to data quality concerns for these groups.
Mean_salary	Average salary (mean, rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.
	These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).
Median_salary	Median salary (rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.
	These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).