Distribution across 10 profiles.
Middle half of Health Data Scientists score between 24% and 33%.
0%
50%
100%
p10 · 20%
38% · p90
Task breakdown by work type
On-screen work70%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen0%
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer + action15%
Computer input, real-world output — needs someone to act on it, not just software.
Fully in-person15%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Health Data Scientist, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Create dashboards and visualizations in tools like Tableau, Power BI, or Python libraries to communicate findings to clinicians, administrators, or stakeholders
deep expertisesocial core
24%DD
6%
Present findings and recommendations in meetings with clinical leadership, regulatory teams, or research collaborators to influence policy or protocol changes
deep expertisesocial core
23%DA
9%
Document data lineage, methodology, code, and assumptions in reports and technical documentation for compliance, reproducibility, and knowledge transfer
17%DD
50%
Collaborate with physicians, epidemiologists, and clinical teams to define data requirements, interpret results, and translate analyses into actionable insights
deep expertisesocial core
16%AA
5%
Perform exploratory data analysis and quality checks to identify missing data, outliers, coding errors, and data integrity issues before modeling
deep expertisesocial element
8%DD
24%
Extract, clean, and validate patient/health records from multiple data sources (EHRs, claims databases, registries) using SQL, Python, or R scripts
5%DD
56%
Build and validate statistical models (regression, survival analysis, risk prediction) to answer clinical research questions or support operational decisions
deep expertisesocial element
4%DD
26%
TaskTimeTypeExposure
Build and validate statistical models (regression, survival analysis, risk prediction) to answer clinical research questions or support operational decisions
deep expertisesocial element
22%DD
29%
Extract, clean, and validate patient/health records from multiple data sources (EHRs, claims databases, registries) using SQL, Python, or R scripts
19%DD
53%
Create dashboards and visualizations in tools like Tableau, Power BI, or Python libraries to communicate findings to clinicians, administrators, or stakeholders
deep expertisesocial core
15%DD
19%
Perform exploratory data analysis and quality checks to identify missing data, outliers, coding errors, and data integrity issues before modeling
deep expertisesocial element
12%DD
27%
Collaborate with physicians, epidemiologists, and clinical teams to define data requirements, interpret results, and translate analyses into actionable insights
deep expertisesocial core
11%AA
5%
Present findings and recommendations in meetings with clinical leadership, regulatory teams, or research collaborators to influence policy or protocol changes
deep expertisesocial core
9%DA
9%
Document data lineage, methodology, code, and assumptions in reports and technical documentation for compliance, reproducibility, and knowledge transfer
9%DD
48%
TaskTimeTypeExposure
Extract, clean, and validate patient/health records from multiple data sources (EHRs, claims databases, registries) using SQL, Python, or R scripts
25%DD
51%
Document data lineage, methodology, code, and assumptions in reports and technical documentation for compliance, reproducibility, and knowledge transfer
23%DD
77%
Build and validate statistical models (regression, survival analysis, risk prediction) to answer clinical research questions or support operational decisions
deep expertisesocial element
20%DD
28%
Create dashboards and visualizations in tools like Tableau, Power BI, or Python libraries to communicate findings to clinicians, administrators, or stakeholders
deep expertisesocial element
14%DD
38%
Collaborate with physicians, epidemiologists, and clinical teams to define data requirements, interpret results, and translate analyses into actionable insights
deep expertisesocial core
9%AA
0%
Present findings and recommendations in meetings with clinical leadership, regulatory teams, or research collaborators to influence policy or protocol changes
deep expertisesocial core
5%DA
2%
Perform exploratory data analysis and quality checks to identify missing data, outliers, coding errors, and data integrity issues before modeling
deep expertisesocial element
1%DD
28%
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AI tools for this role
Tools relevant to the most automatable tasks in this profession.
