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Data Scientist
Based on 40 assessments · 4 from real users
34%
Moderate risk
Average realistic automation risk across all Data Scientist profiles in the dataset.
Score spread
Distribution across 40 profiles.
Middle half of Data Scientists score between 30% and 35%.
0%
50%
100%
Task breakdown by work type
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer input, real-world output — needs someone to act on it, not just software.
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Data Scientist, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
Developing and training machine learning models (e.g., regression, classification, clustering) to solve business problems or make predictions
deep expertise
social element
25%
DD
29%
Exploratory data analysis (EDA) to identify patterns, trends, or anomalies in datasets using statistical methods and visualizations
deep expertise
social element
21%
DD
21%
Cleaning and preprocessing raw data (e.g., handling missing values, removing duplicates, standardizing formats) to prepare it for analysis
deep expertise
18%
DD
37%
Collaborating with stakeholders (e.g., product managers, engineers) to define project goals, interpret results, and align models with business needs
deep expertise
social core
16%
AD
6%
Creating reports, dashboards, or presentations to communicate insights, model performance, or recommendations to non-technical audiences
deep expertise
social element
9%
DA
6%
Optimizing model performance through hyperparameter tuning, feature engineering, or selecting alternative algorithms
deep expertise
7%
DD
38%
Monitoring deployed models in production to track performance, detect drift, and retrain models as needed
1%
DD
62%
Collaborating with stakeholders (e.g., product managers, engineers) to define project goals, interpret results, and align models with business needs
deep expertise
social core
22%
AD
12%
Cleaning and preprocessing raw data (e.g., handling missing values, removing duplicates, standardizing formats) to prepare it for analysis
20%
DD
61%
Optimizing model performance through hyperparameter tuning, feature engineering, or selecting alternative algorithms
deep expertise
18%
DD
44%
Developing and training machine learning models (e.g., regression, classification, clustering) to solve business problems or make predictions
deep expertise
social element
15%
DD
25%
Exploratory data analysis (EDA) to identify patterns, trends, or anomalies in datasets using statistical methods and visualizations
deep expertise
social element
13%
DD
41%
Creating reports, dashboards, or presentations to communicate insights, model performance, or recommendations to non-technical audiences
some context needed
social core
9%
DA
14%
Monitoring deployed models in production to track performance, detect drift, and retrain models as needed
1%
DD
62%
Developing and training machine learning models (e.g., regression, classification, clustering) to solve business problems or make predictions
deep expertise
social element
25%
DD
30%
Cleaning and preprocessing raw data (e.g., handling missing values, removing duplicates, standardizing formats) to prepare it for analysis
21%
DD
67%
Exploratory data analysis (EDA) to identify patterns, trends, or anomalies in datasets using statistical methods and visualizations
deep expertise
social element
16%
DD
28%
Optimizing model performance through hyperparameter tuning, feature engineering, or selecting alternative algorithms
deep expertise
social element
14%
DD
31%
Collaborating with stakeholders (e.g., product managers, engineers) to define project goals, interpret results, and align models with business needs
deep expertise
social core
9%
AD
5%
Monitoring deployed models in production to track performance, detect drift, and retrain models as needed
8%
DD
65%
Creating reports, dashboards, or presentations to communicate insights, model performance, or recommendations to non-technical audiences
deep expertise
social core
4%
DA
0%
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