Skincare Specialists

O*NET-SOC code: 39-5094.00

Provide skincare treatments to face and body to enhance an individual's appearance. Includes electrologists and laser hair removal specialists.

Data source: BLSPublished: 2026-08
Moderate· relative
LowFour relative bandsVery high
Group-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
Source dataset (XLSX download)
Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
Source dataset (CSV download)
Data source: ILOPublished: 2025
0.18
0.09Range of values carried here0.70
Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
Source dataset (PDF download)

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

18 rated tasks · 6 tasks with β ≥ 0.5 (33.3%)

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
All rated tasks are in the O*NET 31.0 task list.

Source: OpenAI "GPTs are GPTs" exposure rubric
Release: gh-main-0471612
License: MIT License, Copyright (c) 2024 OpenAI

Task (O*NET 20.1)	Claude.aiRaw / share %	APIRaw / share %
Advise clients about colors and types of makeup and instruct them in makeup application techniques. 39-5094.00	0.004847.5	—0
Determine which products or colors will improve clients' skin quality and appearance. 39-5094.00	0.003332.7	—0
Examine clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance. 39-5094.00	0.002019.8	—0
Remove body and facial hair by applying wax.	—0	0.002238.2
Treat the facial skin to maintain and improve its appearance, using specialized techniques and products, such as peels and masks.	—0	0.001730.9
Provide facial and body massages.	—0	0.001730.9
Not observed on any surface — 12 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states.
Sterilize equipment and clean work areas.	—0	—0
Keep records of client needs and preferences and the services provided.	—0	—0
Demonstrate how to clean and care for skin properly and recommend skin-care regimens.	—0	—0
Select and apply cosmetic products such as creams, lotions, and tonics.	—0	—0
Cleanse clients' skin with water, creams, or lotions.	—0	—0
Refer clients to medical personnel for treatment of serious skin problems.	—0	—0
Perform simple extractions to remove blackheads.	—0	—0
Apply chemical peels to reduce fine lines and age spots.	—0	—0
Stay abreast of latest industry trends, products, research, and treatments.	—0	—0
Sell makeup to clients.	—0	—0
Collaborate with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care.	—0	—0
Tint eyelashes and eyebrows.	—0	—0

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

Task (O*NET 27.2)	βE1 + 0.5 × E2
Keep records of client needs and preferences and the services provided. O*NET Task ID 8070	1.0
Examine clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance. O*NET Task ID 8072	0.5
Determine which products or colors will improve clients' skin quality and appearance. O*NET Task ID 8077	0.5
Advise clients about colors and types of makeup and instruct them in makeup application techniques. O*NET Task ID 8082	0.5
Sell makeup to clients. O*NET Task ID 8083	0.5
Stay abreast of latest industry trends, products, research, and treatments. O*NET Task ID 20258	0.5
Sterilize equipment and clean work areas. O*NET Task ID 8069	0.0
Demonstrate how to clean and care for skin properly and recommend skin-care regimens. O*NET Task ID 8071	0.0
Select and apply cosmetic products, such as creams, lotions, and tonics. O*NET Task ID 8073	0.0
Cleanse clients' skin with water, creams, or lotions. O*NET Task ID 8074	0.0
Treat the facial skin to maintain and improve its appearance, using specialized techniques and products, such as peels and masks. O*NET Task ID 8075	0.0
Refer clients to medical personnel for treatment of serious skin problems. O*NET Task ID 8076	0.0
Perform simple extractions to remove blackheads. O*NET Task ID 8078	0.0
Provide facial and body massages. O*NET Task ID 8079	0.0
Remove body and facial hair by applying wax. O*NET Task ID 8080	0.0
Apply chemical peels to reduce fine lines and age spots. O*NET Task ID 8081	0.0
Collaborate with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care. O*NET Task ID 8084	0.0
Tint eyelashes and eyebrows. O*NET Task ID 8086	0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page

All O*NET 31.0 task statements (18)

All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.

Task (O*NET 31.0)	Type
Sterilize equipment and clean work areas.	Core
Keep records of client needs and preferences and the services provided.	Core
Demonstrate how to clean and care for skin properly and recommend skin-care regimens.	Core
Examine clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance.	Core
Select and apply cosmetic products, such as creams, lotions, and tonics.	Core
Cleanse clients' skin with water, creams, or lotions.	Core
Treat the facial skin to maintain and improve its appearance, using specialized techniques and products, such as peels and masks.	Core
Refer clients to medical personnel for treatment of serious skin problems.	Core
Determine which products or colors will improve clients' skin quality and appearance.	Core
Perform simple extractions to remove blackheads.	Core
Provide facial and body massages.	Core
Remove body and facial hair by applying wax.	Core
Apply chemical peels to reduce fine lines and age spots.	Core
Tint eyelashes and eyebrows.	Core
Stay abreast of latest industry trends, products, research, and treatments.	Core
Advise clients about colors and types of makeup and instruct them in makeup application techniques.	Supplemental
Sell makeup to clients.	Supplemental
Collaborate with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care.	Supplemental

Anthropic, "What work can robots do?"2026-09

0.12/ 3· Robot exposure index

0 none3 unstructured settings

Scale, basis and source

Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time

Physical tasks among this occupation's O*NET 29.3 tasks

Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.

Source dataset

The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.

Tier of each physical task

Robot environment tiers (E0–E3)

E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site

Only tasks classified as physical are listed; other tasks have no tier in the source.

Task (O*NET 29.3)	Robot environment tier
Sterilize equipment and clean work areas.	E0 none
Examine clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance.	E2 structured facilities
Select and apply cosmetic products, such as creams, lotions, and tonics.	E0 none
Cleanse clients' skin with water, creams, or lotions.	E0 none
Treat the facial skin to maintain and improve its appearance, using specialized techniques and products, such as peels and masks.	E0 none
Perform simple extractions to remove blackheads.	E0 none
Provide facial and body massages.	E0 none
Remove body and facial hair by applying wax.	E0 none
Apply chemical peels to reduce fine lines and age spots.	E0 none
Collaborate with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care.	E0 none
Tint eyelashes and eyebrows.	E0 none

Employment outlook (BLS)

Employment, 2025: 104K
Projected employment, 2035: 113K
Change (2025–35): +8.8%
Median annual wage, 2025: $45,330
Typical education needed for entry: Postsecondary nondegree award

lowest $30,890median wage across 825 detailed occupations, BLS EP 2025–35highest $559,030

Source: U.S. Bureau of Labor Statistics, Employment Projections 2025–35 — Employment by detailed occupation, 2025 and projected 2035

Korea classification links (KSCO)

Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.

KSCO code	KSCO title	Relation	Status
43141	피부 관리사	Same scope	confirmed
43149	그 외 피부 및 체형 관리 종사원	This occupation is broader	confirmed

Relation and status definitions(kr-direct v1.1)

ILO occupational description

AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.

ISCO-08 5142Beauticians and Related Workers

The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.

Beauticians and related workers, give facial and body beauty treatments, apply cosmetics and make-up and give other kinds of treatment to individuals in order to improve their appearance.

Tasks

(a) cleaning and applying creams, lotions and related products to face and other parts of the body;
(b) giving facial and body massage;
(c) applying make-up to clients of a beauty parlour or to actors and other performers;
(d) cleaning, shaping and polishing finger- and toe-nails and treating ailments of the human foot such as corns, calluses or deformed toe-nails;
(e) attending to clients taking baths and administering elementary massage;
(f) using waxing, sugaring and depilation techniques to remove unwanted bodily hair;
(g) advising clients on diet and exercise to assist in weight loss and slimming;
(h) arranging appointments and collecting payments.

Example occupations

Bath attendant · Beautician · Make-up artist · Manicurist · Pedicurist · Slimming consultant

Sources: ILO ISCO-08 — details: Credits

ESCO occupational vocabulary

Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.

aestheticianESCO 5142.1Same scope
Aestheticians offer skin care treatments. They apply various facial treatments according to their clients' needs and skin type, such as lotions, scrubs, peels and masks, in order to maintain the skin healthy and attractive. Aestheticians may also give neck massages and body treatments such as wraps. Aestheticians remove unwanted hair on different body parts like eyebrows, upper lip or the bikini area. They perform facial massage and apply make-up for various occasions.
hair removal technicianESCO 5142.3This occupation is broader
Hair removal technicians provide cosmetic services to their clients by removing unwanted hair on various body parts. They can use different techniques for temporary hair removal, such as epilation and depilation techniques, or permanent hair removal methods, such as electrolysis or intense pulsed light.

Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)

License: CC BY 4.0 (EU Reuse Notice)

View original

ESCO reuse terms and the modification notice are in Credits.

France classification links (ROME)

AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.

ROME code	ROME title (French)	Relation
D1208	Esthéticien / Esthéticienne	Same scopeprovisional
D1237	Socio-esthéticien / Socio-esthéticienne	This occupation is broaderprovisional
D1231	Responsable espace bien-être	Related, not a matchprovisional

Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.

ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.

(AI Changing Work link release: fr-direct v1)

Spain classification links (CNO-11)

AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.

CNO-11 code	CNO-11 title (Spanish)	Relation
5812	Especialistas en tratamientos de estética, bienestar y afines	This occupation is narrowerprovisional

Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.

(AI Changing Work link release: es-direct v1)

Germany classification links (KldB)

AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.

KldB code	KldB title (German)	Relation
82322	Berufe in der Kosmetik - fachlich ausgerichtete Tätigkeiten	This occupation is narrower

Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.

(AI Changing Work link release: de-direct v1)

Japan classification links (JSCO)

AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.

JSCO code	JSCO title (Japanese)	Relation
383	美容サービス従事者（美容師を除く）	This occupation is narrowerprovisional

Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.

(AI Changing Work link release: jp-direct v1)

Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources