Metro Area
AI Job Risk in Scranton--Wilkes-Barre--Hazleton, PA
Scranton--Wilkes-Barre--Hazleton, PA scores 51.0/100 for AI job risk, ranking #243 out of 396 us metro areas. That puts it around the middle — a moderate mix of AI-vulnerable and AI-resistant jobs in the local economy.
Ranked #243 of 396 us metro areas
Rank
#243
around the middle
Total Workforce
260 K
jobs analysed
High-Risk Jobs
7.8 K
3% of workforce
Out of
396
us metro areas
Most at Risk
Jobs in Scranton--Wilkes-Barre--Hazleton, PA most likely to be automated or transformed by AI
Data Entry Keyer
230 employed
Telemarketer
170 employed
Transcriptionist
50 employed
Bookkeeper
2.1 K employed
Customer Service Representative
4.2 K employed
Tax Preparer
90 employed
Computer Programmer
130 employed
Court Reporter
40 employed
Software Developer
710 employed
Web Developer
40 employed
Safest from AI
Jobs in Scranton--Wilkes-Barre--Hazleton, PA least likely to be affected by AI
Tree Trimmer
50 employed
Landscaper
1.4 K employed
Roofer
160 employed
Zookeeper
320 employed
Lifeguard
110 employed
Janitor
3.6 K employed
Concrete Finisher
120 employed
Massage Therapist
90 employed
Painter
160 employed
Housekeeper
1 K employed
What this means
The exposure score (51.0/100) measures how much of Scranton--Wilkes-Barre--Hazleton, PA's workforce is in jobs that AI can automate or significantly change. It's not a prediction that jobs will disappear — it's a measure of how exposed the local economy is to AI-driven change.
How it works: We score 289 occupations from 0 (AI has little impact) to 100 (AI can do most of the job) using 10 research sources. We then match these scores to real employment data for Scranton--Wilkes-Barre--Hazleton, PA, weighting by how many people actually work in each role.
High-risk jobs (score 60+) include roles like customer service, data entry, and bookkeeping where AI can already handle most tasks. Low-risk jobs (score under 30) include trades, healthcare, and social work where physical presence or human judgement is essential.
Full methodology · Data covers 39% of occupation categories in this area