Role Definition
| Field | Value |
|---|---|
| Job Title | Biological Scientists, All Other (BLS SOC 19-1029) |
| Seniority Level | Mid-Level (3-8 years post-graduate, independent research capability) |
| Primary Function | Catch-all BLS category for biological scientists not classified elsewhere — includes bioinformatics scientists, molecular and cellular biologists, geneticists, and general biologists. Designs and conducts experiments, analyses biological data, develops research methodologies, publishes findings, writes grants, and collaborates across institutions. Works in government (largest employer), pharmaceutical/biotech R&D, and academia. |
| What This Role Is NOT | Not a medical scientist (SOC 19-1042, disease-focused research — scored 54.5 Green). Not a microbiologist (SOC 19-1022). Not a zoologist or wildlife biologist (SOC 19-1023). Not a biochemist/biophysicist (SOC 19-1021). Not a biological technician (protocol execution — scored 28.2 Yellow). Not a biological science teacher (SOC 25-1042 — scored 62.0 Green). |
| Typical Experience | PhD or Master's in biology, molecular biology, genetics, ecology, or related field. 3-8 years post-degree. Some roles require only a Master's for government or industry positions. |
Seniority note: Junior (postdoc or early-career, 0-2 years) would score lower Yellow or Red — more routine lab execution, less grant strategy. Senior PIs and research directors with established programmes would score Green (Transforming, ~52-56) due to leadership accountability and institutional positioning.
Protective Principles + AI Growth Correlation
| Principle | Score (0-3) | Rationale |
|---|---|---|
| Embodied Physicality | 1 | Wet lab and field work — cell culture, tissue processing, specimen collection, microscopy, environmental sampling — but within structured laboratory or defined field environments. Lab robotics handle some routine physical tasks. |
| Deep Interpersonal Connection | 1 | Collaborates with research teams, mentors junior scientists, builds networks across institutions. Professional relationships matter for grant success and collaborative science, but trust is not the primary value proposition. |
| Goal-Setting & Moral Judgment | 2 | Formulates research questions and experimental approaches in areas with limited precedent. Makes ethical decisions about research direction (biosafety, IRB compliance, environmental impact). Significant but not as frontier-defining as medical scientists pursuing novel disease mechanisms. |
| Protective Total | 4/9 | |
| AI Growth Correlation | 0 | AI adoption neither directly creates nor destroys demand for biological scientists. Demand driven by government funding levels, environmental policy, agricultural needs, and basic science priorities. AI makes biologists more productive but does not change whether they are needed. |
Quick screen result: Protective 4/9 with moderate goal-setting. Likely Yellow or borderline Green — proceed to quantify with task analysis.
Task Decomposition (Agentic AI Scoring)
| Task | Time % | Score (1-5) | Weighted | Aug/Disp | Rationale |
|---|---|---|---|---|---|
| Hypothesis generation & experimental design | 20% | 2 | 0.40 | AUGMENTATION | AI tools synthesise literature and suggest knowledge gaps (Elicit, Semantic Scholar). But formulating genuinely novel research questions requires deep domain expertise, intuition from years of experimental iteration, and creative reasoning no current AI replicates. The scientist defines what to investigate and how. |
| Laboratory/field research execution | 25% | 2 | 0.50 | AUGMENTATION | Physical lab and field work — cell culture, specimen collection, molecular assays (PCR, Western blots, sequencing), environmental sampling. AI-guided robotics entering high-throughput screening but complex assay troubleshooting and novel protocol development remain human-led. Scientist adapts in real time. |
| Data analysis & interpretation | 20% | 3 | 0.60 | AUGMENTATION | AI handles significant sub-workflows: bioinformatics pipelines, image analysis (microscopy, flow cytometry), statistical modelling, genomics/proteomics pattern recognition. Scientist leads interpretation, validates biological significance, and determines what the data means in context of the research question. |
| Scientific writing & publication | 15% | 3 | 0.45 | AUGMENTATION | AI drafts sections, handles reference management, assists with figure generation and revisions. Framing discoveries, arguing for significance, and navigating peer review requires scientific expertise. AI handles sub-workflows but the scientist leads the intellectual narrative. |
| Grant writing & funding acquisition | 10% | 2 | 0.20 | AUGMENTATION | AI assists with literature review, section drafting, and editing. Core work — identifying knowledge gaps, articulating scientific significance, and persuading expert reviewers — requires deep judgment and domain credibility. |
| Lab/team management & mentoring | 10% | 1 | 0.10 | NOT INVOLVED | Training junior researchers, managing lab budgets, building collaborative networks, ensuring biosafety compliance. Human relationships and mentorship that AI cannot perform. |
| Total | 100% | 2.25 |
Task Resistance Score: 6.00 - 2.25 = 3.75/5.0
Displacement/Augmentation split: 0% displacement, 90% augmentation, 10% not involved.
Reinstatement check (Acemoglu): AI creates new tasks within this role: validating AI-predicted protein structures (AlphaFold) against experimental data, interpreting AI-generated genomic/proteomic analyses, curating training data for domain-specific ML models, designing experiments to test computationally generated hypotheses, and bridging bench science with computational biology. The role is expanding its scope, not shrinking — but the new tasks require scientists who can work at the human-AI interface.
Evidence Score
| Dimension | Score (-2 to 2) | Evidence |
|---|---|---|
| Job Posting Trends | 0 | BLS projects 1-2% growth 2024-2034 ("slower than average"), 4,800 annual openings from 63,700 base. No Bright Outlook designation for this SOC. Stable but not growing. Life sciences hiring broadly increasing (EPM Scientific 2026) but concentrated in medical scientists and bioinformatics — not this catch-all category specifically. |
| Company Actions | 0 | Pharma/biotech investment in AI R&D growing ($3B+ annually) but augmenting scientists, not eliminating them. Biopharma layoffs (42,700 in 2025) driven by patent cliffs and restructuring, not AI displacement. No major company actions specifically targeting "all other" biological scientists — no signal either way. |
| Wage Trends | 0 | BLS median $93,330 ($44.87/hr, 2024). Modest, stable. Industry roles pay more ($110K+), government and academic roles lower. Growth tracking inflation. No surging demand premiums for this category specifically, though computational biology skills command premiums across all biological science roles. |
| AI Tool Maturity | 0 | Production tools augment core tasks: AlphaFold 3 (protein structure), bioinformatics pipelines (genomics/proteomics), Elicit/Semantic Scholar (literature), automated image analysis (microscopy). All require scientist oversight. Tools create new validation work within the role. Not displacing, but significantly changing data analysis workflows. |
| Expert Consensus | 1 | Universal consensus across McKinsey, industry analysts, and academic bodies: AI augments biological scientists, transforms workflows, does not displace. "AI fluency" is the top differentiator for career advancement (EPM Scientific, Applied Clinical Trials). No credible source predicts biological scientist displacement. |
| Total | 1 |
Barrier Assessment
Reframed question: What prevents AI execution even when programmatically possible?
| Barrier | Score (0-2) | Rationale |
|---|---|---|
| Regulatory/Licensing | 1 | PhD or Master's required by convention (not formal licensure). Biosafety regulations (BSL labs) require qualified personnel. IRB mandates human investigators for human subjects research. FDA requires qualified investigators for any clinical-adjacent work. No regulatory pathway for autonomous AI-led biological research. |
| Physical Presence | 1 | Wet lab and field work require physical presence — biological specimens, cell culture, environmental sampling, equipment operation. Structured laboratory environments. Computational work can be remote but experimental research cannot be. |
| Union/Collective Bargaining | 0 | Scientists are not unionised. Some postdoc unions emerging at major universities but minimal protection for mid-level independent researchers in government or industry. |
| Liability/Accountability | 1 | PIs bear personal accountability for research integrity — data fabrication leads to retracted papers, career destruction, and potential debarment from federal funding. Government scientists bear accountability for regulatory decisions (EPA, USDA, FDA). Not malpractice-level but career-ending professional consequences ensure human accountability. |
| Cultural/Ethical | 1 | Scientific community values human-driven discovery and intellectual contribution. Journals require AI use disclosure. Peer review assumes human authorship and judgment. Grant agencies fund investigators, not algorithms. Regulatory bodies require human oversight for biological research with safety implications. |
| Total | 4/10 |
AI Growth Correlation Check
Confirmed 0 (Neutral). AI adoption does not inherently create or destroy demand for biological scientists in this category. Demand is driven by government research funding (NIH, NSF, EPA, USDA), pharmaceutical R&D investment, environmental policy, agricultural priorities, and basic science needs. AI tools increase scientist productivity — enabling each researcher to process more data and test more hypotheses — but the fundamental need for human-led biological research is unchanged. Not Accelerated Green (no recursive AI dependency). Not negative (AI makes the role more productive, not obsolete).
JobZone Composite Score (AIJRI)
| Input | Value |
|---|---|
| Task Resistance Score | 3.75/5.0 |
| Evidence Modifier | 1.0 + (1 × 0.04) = 1.04 |
| Barrier Modifier | 1.0 + (4 × 0.02) = 1.08 |
| Growth Modifier | 1.0 + (0 × 0.05) = 1.00 |
Raw: 3.75 × 1.04 × 1.08 × 1.00 = 4.2120
JobZone Score: (4.2120 - 0.54) / 7.93 × 100 = 46.3/100
Zone: YELLOW (Green >= 48, Yellow 25-47, Red <25)
Sub-Label Determination
| Metric | Value |
|---|---|
| % of task time scoring 3+ | 35% |
| AI Growth Correlation | 0 |
| Sub-label | Yellow (Moderate) — AIJRI 25-47, <40% task time scores 3+ |
Assessor override: None — formula score accepted. The 46.3 places this role 1.7 points below the Green boundary. This borderline position accurately reflects the tension: strong task resistance (3.75, identical to medical scientists) dragged into Yellow by weak BLS growth projections and neutral market evidence for this specific SOC category. The "All Other" catch-all lacks the targeted demand signal that pushes medical scientists (19-1042) into Green.
Assessor Commentary
Score vs Reality Check
The 46.3 AIJRI places this role 1.7 points below the Green/Yellow boundary — genuinely borderline. The task resistance (3.75) is strong and identical to medical scientists (54.5 Green), but the evidence gap is the differentiator: medical scientists benefit from 9% BLS growth, Bright Outlook status, and targeted pharma hiring, while this "All Other" category projects only 1-2% growth with no Bright Outlook designation. The role is not barrier-dependent: stripping barriers entirely would yield ~42.1 — still Yellow. The formula correctly captures that resistant tasks in a flat-growth market are not the same as resistant tasks in a booming one. No override applied because the weak BLS signal for this specific SOC is genuine, not a data artefact.
What the Numbers Don't Capture
- The "All Other" catch-all masks internal divergence. This SOC includes bioinformatics scientists (high AI demand, premium salaries), molecular biologists (strong pharma demand), geneticists (growing clinical demand), and general biologists (flat government research positions). The average score masks wildly different trajectories within the same SOC code.
- Government vs industry divergence. Government is the largest employer for this SOC (BLS top industry). Government biologists face flat budgets, hiring freezes, and limited growth. Industry biologists at AI-first biotechs face entirely different conditions — acute demand, premium compensation, and rapid tool adoption.
- Function-spending vs people-spending. Life sciences R&D spending grows ($3B+ on AI alone), but some investment goes to platforms, automation infrastructure, and computational tools — not headcount. Productivity gains per scientist may reduce total headcount needed per unit of research output.
- AI productivity paradox. If AI tools make each scientist 2-3x more productive, fewer scientists may be needed overall. The research question space is currently expanding faster than productivity gains, but this is the long-term risk for any scientist role.
Who Should Worry (and Who Shouldn't)
Bioinformatics scientists and computational biologists within this SOC should not worry at all — they are in acute demand and would individually score Green if assessed separately. Molecular biologists and geneticists in pharma/biotech are similarly well-positioned, with strong industry demand and AI creating new validation work. Government biologists in stable research programmes are in the most borderline position — the role itself resists automation, but flat growth and budget constraints mean fewer positions over time. The most exposed are mid-level biologists doing routine analytical work (taxonomy, environmental monitoring, quality control testing) where AI-powered image recognition and automated analysis are most mature. The single biggest factor separating the safe version from the at-risk version is whether you are generating novel research questions or executing established analytical protocols. The hypothesis-generating scientist who bridges wet lab and computational work is untouchable. The biologist running standardised assays against known protocols is increasingly augmented to the point where fewer are needed.
What This Means
The role in 2028: Biological scientists will use AI as standard research infrastructure — AlphaFold for protein structure, generative models for experimental design suggestions, AI literature synthesis for grant writing, ML-powered analysis pipelines for genomics/proteomics data, and automated image analysis for microscopy. Data analysis workflows will be heavily AI-accelerated. But the scientist still generates every hypothesis, designs every experiment, validates every AI prediction against wet-lab or field reality, and bears accountability for every published result.
Survival strategy:
- Develop computational fluency — learn Python/R, basic ML concepts, and how to critically evaluate AI-generated predictions. The biologist who bridges wet lab/field work and computational science is most valuable.
- Specialise in areas where AI creates new work — AI prediction validation, computational-experimental integration, interdisciplinary research that moves AI insights into biological applications.
- Move toward industry or translational roles if currently in flat-growth government positions. Industry demand for AI-fluent biological scientists is growing faster than academic or government demand.
Where to look next. If you're considering a career shift, these Green Zone roles share transferable skills with biological science:
- Medical Scientist (AIJRI 54.5) — Directly adjacent; disease-focused research with stronger market demand and regulatory barriers.
- Natural Sciences Manager (AIJRI 51.6) — Leadership transition leveraging research programme management, grant strategy, and team oversight experience.
- AI Auditor (AIJRI 64.5) — For computationally fluent biologists; scientific method, data validation, and systematic evaluation skills transfer directly to auditing AI systems.
Browse all scored roles at jobzonerisk.com to find the right fit for your skills and interests.
Timeline: 5-10 years. Constrained by the irreducibility of the scientific method (hypothesis, experiment, interpretation, iteration), graduate training pipeline (5-10 years minimum), regulatory mandates for human oversight in biological research, and the expanding frontier of unanswered biological questions. The timeline is shorter than medical scientists because the BLS growth signal is weaker and the catch-all nature of this SOC includes roles more exposed to AI automation.
