Natural Sciences Managers
Natural Sciences Managers — AI exposure, safer roles, and a pivot plan.
Also known as: Geological Manager · Geochemical Manager · Clinical Trials Manager · Clinical Project Manager · Analytical Services Manager · Chemical Engineer Supervisor
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
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Determine scientific or technical goals within broad outlines provided by top management and make detailed plans to accomplish these goals.61
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Develop innovative technology or train staff for its implementation.59
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Develop or implement policies, standards, or procedures for the architectural, scientific, or technical work performed to ensure regulatory compliance or operations enhancement.58
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Confer with scientists, engineers, regulators, or others to plan or review projects or to provide technical assistance.57
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
The current data release does not distinguish durable tasks for this role.
Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 50 puts Natural Sciences Managers in the third quartile of analyzed occupations. In practice, exposure this high is about the mix: 1 of 16 analyzed tasks lean automatable, 15 augmentable, and 0 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.
One next move: audit how much of your week sits in the exposed tasks above — then shift time toward the durable set or investigate the adjacent roles below.
Lower-exposure adjacent roles
No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.
Labor-market context
- $167,220median wage
- 108,690employed
- 8,500annual openings
- +3.7%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Your week probably doesn’t match the average
This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.
Personalize my result — $9Related roles
Adjacent by skills or family — no exposure claim implied.
FAQ — Natural Sciences Managers
- What does a score of 50 mean for a Natural Sciences Managers?
- It means that, weighted across the 16 tasks we analyzed for this role, the task mix sits at 50 on a 0–100 exposure scale — in the third quartile of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
- Which tasks in this role are most exposed to AI?
- The highest-exposure tasks are: Determine scientific or technical goals within broad outlines provided by top management and make detailed plans to accomplish these goals. Exposure is scored per task from structured data, not generated by a language model.
- Which parts of this job are most durable?
- The current data release does not distinguish durable tasks for this role.
- Is this score personalized to me?
- No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.
Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 16 of 16 tasks carry source-level provenance · methodology