Food Science Technicians
Work with food scientists or technologists to perform standardized qualitative and quantitative tests to determine physical or chemical properties of food or beverage products. Includes technicians who assist in research and development of production technology, quality control, packaging, processing, and use of foods.
Also known as: Beer Brewer · Cream Tester · Dairy Tester · Biotechnician · Cheese Tester · Butter Fat Tester
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.
Your free role snapshot
Start with the work, not the headline
This occupation baseline already contains different kinds of work. These are the two ends of its current task range and one useful action you can take now.
Most exposed task
Record or compile test results or prepare graphs, charts, or reports.
Augmentable 70/100 exposure
Most durable task
Taste or smell foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics.
Durable 10/100 exposure
Useful next step
Make one change to the task mix
Compare one real week with the exposed tasks, test one supervised delegation, and redirect the saved time toward more durable work.
Want this mapped to your actual week? Know what to strengthen, what to delegate to AI with review, and what to do over the next 7, 30, and 90 days.
Build my 7/30/90-day action plan — $9Most exposed tasks
No task is classified fully automatable in this release. The highest-exposure task relative to this role is shown in the free snapshot above; the assistive work is detailed next.
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Record or compile test results or prepare graphs, charts, or reports.70
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Maintain records of testing results or other documents as required by state or other governing agencies.65
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Analyze test results to classify products or compare results with standard tables.60
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
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Taste or smell foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics.10
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Perform regular maintenance of laboratory equipment by inspecting, calibrating, cleaning, or sterilizing.20
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Prepare or incubate slides with cell cultures.20
Task exposure values and classifications are published, versioned estimates audited against the anchored rubric. This page reads them from the data release rather than generating them at request time. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 38 puts Food Science Technicians in the third quartile of analyzed occupations. In practice, exposure this high is about the mix: 0 of 16 analyzed tasks lean automatable, 8 augmentable, and 8 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.
Lower-exposure adjacent roles
Shown only when the target is at least 10 points lower under the same score version and skill overlap is at least 50%. These are adjacent roles with lower task exposure — not guaranteed “safe careers”.
Labor-market context
- $52,130median wage
- 14,600employed
- 3,200annual openings
- +4.4%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Turn your actual week into a plan
Know which work to strengthen, what to delegate to AI with review, and what to do over the next 7, 30, and 90 days. Your $9 plan includes a confidence-rated personal score, task matrix, human moat, and only genuinely lower-exposure adjacent roles.
Build my 7/30/90-day action plan — $9Related roles
Adjacent by skills or family — no exposure claim implied.
How this score was calculated
The score is the plain average of the AI-exposure values of this role’s 16 analyzed tasks (equal task weights in this release — no hidden factors):
- Augmentable 8 tasks, averaging 53/100 exposure
- Durable 8 tasks, averaging 24/100 exposure
score = (8×53 + 8×24) ÷ 16 ≈ 38
Every task’s exposure value was scored against a published anchored rubric that scores tasks as actually performed — physical, in-person, and accountability requirements included (data release 2026.07.12-r2). The band (High) is a corpus quartile; the percentile is tie-aware across all 968 occupations.
FAQ — Food Science Technicians
- Will AI replace Food Science Technicians?
- This score cannot predict whether this occupation or any individual job will be replaced. It shows which tasks current and near-term AI can perform or accelerate, which tasks remain more durable, and where a worker can adapt. Employer decisions, demand, regulation, and how the role changes are outside the score.
- What does a score of 38 mean for a Food Science Technicians?
- It means that, weighted across the 16 tasks we analyzed for this role, the task mix sits at 38 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?
- This role has no strongly automatable task in the current data release.
- Which parts of this job are most durable?
- The most durable responsibilities are: Taste or smell foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics; Perform regular maintenance of laboratory equipment by inspecting, calibrating, cleaning, or sterilizing; Prepare or incubate slides with cell cultures. Durable tasks typically depend on judgment, relationships, physical presence, or accountability.
- 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 2026.07.12-r2 · updated 2026-07-13 · baseline mapping: 16 of 16 tasks carry source-level provenance · methodology