π‘ Data Scientists have a composite risk score of 45/100 (Frey-Osborne probability: 40%, GenAI exposure: 81/100). With 233,440 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 233,440 workers and a median wage of $113K,data scientists represent a significant portion of the US workforce. Their GenAI exposure index is 81%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
45/100
Employment
233,440
Median Wage
$113K
GenAI Exposure
81%
β οΈ Top Risk Factors
Low-code / no-code platforms reducing custom development
AI pair-programming and code generation tools
Machine learning model auto-tuning displacing ML engineers
Large language model automation of analysis tasks
π‘οΈ Tasks AI Can't Easily Replace
Architecting novel systems requiring creative problem-solving
Crisis debugging of complex production incidents
Mentoring junior developers and team leadership
Stakeholder negotiation and requirements elicitation
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Advertising, Marketing, Promotions, Public Relations, and Sales Managers
63% skills overlap Β· $145K median wage
Mathematicians
72% skills overlap Β· $122K median wage
Computer and Information Research Scientists
75% skills overlap Β· $141K median wage
β Frequently Asked Questions
Will AI completely replace data scientists?
Possible. Significant task automation is underway β workers should actively upskill.
What is the AI risk score for data scientists?
Data Scientists have a composite AI automation risk score of 45 out of 100, classified as "Elevated".
How many data scientists are there in the US?
There are approximately 233,440 data scientists employed in the United States.
What do data scientists earn?
The median annual wage for data scientists is $113K.
What skills should data scientists develop?
Focus on tasks AI can't easily replicate: architecting novel systems requiring creative problem-solving, crisis debugging of complex production incidents, mentoring junior developers and team leadership, stakeholder negotiation and requirements elicitation. These human-centric skills will become more valuable as routine tasks are automated.