π‘ Statisticians have a composite risk score of 41/100 (Frey-Osborne probability: 22%, GenAI exposure: 86/100). With 29,800 workers in the US, this occupation faces moderate but manageable AI pressure. Full occupation profile β
π― The Verdict
Possible. Significant task automation is underway β workers should actively upskill.
With 29,800 workers and a median wage of $103K,statisticians represent a significant portion of the US workforce. Their GenAI exposure index is 86%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
41/100
Employment
29,800
Median Wage
$103K
GenAI Exposure
86%
β οΈ Top Risk Factors
AI summarization replacing manual report compilation
AI-generated written content replacing manual drafting
Machine learning model auto-tuning displacing ML engineers
Chatbot displacement of customer-facing interactions
π‘οΈ Tasks AI Can't Easily Replace
Architecting novel systems requiring creative problem-solving
Cross-functional collaboration on ambiguous problems
Stakeholder negotiation and requirements elicitation
Ethical AI oversight and bias auditing
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Advertising, Marketing, Promotions, Public Relations, and Sales Managers
55% skills overlap Β· $145K median wage
Mathematicians
79% skills overlap Β· $122K median wage
Computer Occupations
75% skills overlap Β· $106K median wage
β Frequently Asked Questions
Will AI completely replace statisticians?
Possible. Significant task automation is underway β workers should actively upskill.
What is the AI risk score for statisticians?
Statisticians have a composite AI automation risk score of 41 out of 100, classified as "Elevated".
How many statisticians are there in the US?
There are approximately 29,800 statisticians employed in the United States.
What do statisticians earn?
The median annual wage for statisticians is $103K.
What skills should statisticians develop?
Focus on tasks AI can't easily replicate: architecting novel systems requiring creative problem-solving, cross-functional collaboration on ambiguous problems, stakeholder negotiation and requirements elicitation, ethical ai oversight and bias auditing. These human-centric skills will become more valuable as routine tasks are automated.