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Research Scientist, Artificial Intelligence
Based on 10 assessments · 1 from real users
33%
Moderate risk
Average realistic automation risk across all Research Scientist, Artificial Intelligence profiles in the dataset.
Score spread
Distribution across 10 profiles.
Middle half of Research Scientist, Artificial Intelligences score between 32% 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 Research Scientist, Artificial Intelligence, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
Profile 1 · 26%
Profile 2 · 33%
Profile 3 · 36%
Presenting findings at lab meetings, conferences, or to stakeholders; creating slides and explaining technical concepts
some context needed
social core
25%
DA
12%
Analyzing experimental results, visualizing data, debugging models that underperform, investigating unexpected behaviors
deep expertise
social element
20%
DD
31%
Collaborating with team members: discussing ideas in meetings, code review, brainstorming problem-solving approaches
deep expertise
social element
17%
AA
6%
Designing and implementing novel machine learning model architectures, loss functions, and training procedures in code
deep expertise
17%
DD
37%
Writing and iterating on research papers: drafting methods, results sections, responding to reviewer feedback
deep expertise
social element
9%
DD
22%
Running experiments: setting up datasets, hyperparameter tuning, running training jobs, tracking results and metrics
8%
DD
87%
Literature review and reading recent papers/preprints to understand state-of-the-art methods and findings in their research area
deep expertise
social element
1%
DD
29%
Designing and implementing novel machine learning model architectures, loss functions, and training procedures in code
deep expertise
23%
DD
36%
Analyzing experimental results, visualizing data, debugging models that underperform, investigating unexpected behaviors
deep expertise
social element
18%
DD
30%
Running experiments: setting up datasets, hyperparameter tuning, running training jobs, tracking results and metrics
17%
DD
63%
Literature review and reading recent papers/preprints to understand state-of-the-art methods and findings in their research area
deep expertise
social element
16%
DD
34%
Presenting findings at lab meetings, conferences, or to stakeholders; creating slides and explaining technical concepts
some context needed
social core
11%
DA
15%
Collaborating with team members: discussing ideas in meetings, code review, brainstorming problem-solving approaches
deep expertise
social core
9%
AA
0%
Writing and iterating on research papers: drafting methods, results sections, responding to reviewer feedback
deep expertise
social core
2%
DD
19%
Running experiments: setting up datasets, hyperparameter tuning, running training jobs, tracking results and metrics
27%
DD
63%
Writing and iterating on research papers: drafting methods, results sections, responding to reviewer feedback
deep expertise
social core
15%
DD
22%
Analyzing experimental results, visualizing data, debugging models that underperform, investigating unexpected behaviors
deep expertise
social element
14%
DD
36%
Designing and implementing novel machine learning model architectures, loss functions, and training procedures in code
deep expertise
13%
DD
38%
Literature review and reading recent papers/preprints to understand state-of-the-art methods and findings in their research area
deep expertise
social element
13%
DD
31%
Collaborating with team members: discussing ideas in meetings, code review, brainstorming problem-solving approaches
deep expertise
social core
11%
AA
3%
Presenting findings at lab meetings, conferences, or to stakeholders; creating slides and explaining technical concepts
deep expertise
social core
4%
DA
8%
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