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Director Of Data Science
Based on 10 assessments
27%
Moderate risk
Average realistic automation risk across all Director Of Data Science profiles in the dataset.
Score spread
Distribution across 10 profiles.
Middle half of Director Of Data Sciences score between 22% and 31%.
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 Director Of Data Science, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
Review and mentor data scientists and engineers on their work, code reviews, and technical growth
deep expertise
social core
26%
DA
4%
Attend meetings with product, engineering, and business teams to understand requirements and constraints
deep expertise
social core
23%
AA
5%
Scope and define new data science problems, align with business objectives
some context needed
social core
16%
AD
22%
Build and validate machine learning models (training, hyperparameter tuning, evaluation)
16%
DD
58%
Lead strategy discussions and roadmap planning with stakeholders and executives
deep expertise
social core
8%
DA
0%
Prepare reports, dashboards, and presentations translating model results for non-technical audiences
deep expertise
social element
6%
DD
29%
Wrangle, clean, and explore datasets; identify quality issues and data gaps
0%
DD
61%
Prepare reports, dashboards, and presentations translating model results for non-technical audiences
28%
DD
47%
Review and mentor data scientists and engineers on their work, code reviews, and technical growth
deep expertise
social core
25%
DA
2%
Scope and define new data science problems, align with business objectives
deep expertise
social core
17%
AD
8%
Wrangle, clean, and explore datasets; identify quality issues and data gaps
15%
DD
66%
Attend meetings with product, engineering, and business teams to understand requirements and constraints
deep expertise
social core
6%
AA
2%
Lead strategy discussions and roadmap planning with stakeholders and executives
deep expertise
social core
5%
DA
7%
Build and validate machine learning models (training, hyperparameter tuning, evaluation)
1%
DD
57%
Build and validate machine learning models (training, hyperparameter tuning, evaluation)
26%
DD
67%
Wrangle, clean, and explore datasets; identify quality issues and data gaps
21%
DD
69%
Scope and define new data science problems, align with business objectives
deep expertise
social element
16%
AD
20%
Attend meetings with product, engineering, and business teams to understand requirements and constraints
deep expertise
social core
15%
AA
2%
Lead strategy discussions and roadmap planning with stakeholders and executives
deep expertise
social core
13%
DA
0%
Prepare reports, dashboards, and presentations translating model results for non-technical audiences
5%
DD
47%
Review and mentor data scientists and engineers on their work, code reviews, and technical growth
deep expertise
social core
0%
DA
3%
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