# Forensic Science Technicians

> Collect, identify, classify, and analyze physical evidence related to criminal investigations. Perform tests on weapons or substances, such as fiber, hair, and tissue to determine significance to investigation. May testify as expert witnesses on evidence or crime laboratory techniques. May serve as specialists in area of expertise, such as ballistics, fingerprinting, handwriting, or biochemistry.

- **SOC code:** 19-4092.00
- **Canonical URL:** https://singulariki.com/roles/role-19-4092-00
- **Also known as:** CSI (Crime Scene Investigator), Crime Scene Technician (Crime Scene Tech), Criminalist, Forensic Scientist, Crime Lab Analyst (Crime Laboratory Analyst), Crime Scene Analyst (CSA), Evidence Technician (Evidence Tech), Forensic Science Examiner
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Collect evidence from crime scenes, storing it in conditions that preserve its integrity.
- Keep records and prepare reports detailing findings, investigative methods, and laboratory techniques.
- Use photographic or video equipment to document evidence or crime scenes.
- Testify in court about investigative or analytical methods or findings.
- Use chemicals or other substances to examine latent fingerprint evidence and compare developed prints to those of known persons in databases.
- Measure and sketch crime scenes to document evidence.
- Visit morgues, examine scenes of crimes, or contact other sources to obtain evidence or information to be used in investigations.
- Train new technicians or other personnel on forensic science techniques.
- Operate and maintain laboratory equipment and apparatus.
- Examine physical evidence, such as hair, biological fluids, fiber, wood, or soil residues to obtain information about its source and composition.
- Collect impressions of dust from surfaces to obtain and identify fingerprints.
- Reconstruct crime scenes to determine relationships among pieces of evidence.

**Emerging tasks** (O*NET):
- Enter data into databases.
- Operate drones to capture aerial footage or photographs of crime scenes for further analysis.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Law and Government _(knowledge)_
- Public Safety and Security _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Writing _(essential_skill)_
- Speaking _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Written Expression _(ability)_
- Inductive Reasoning _(ability)_

**Skills in demand:**
- Writing _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_
- English Language _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Outlook _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Adobe Photoshop _(hot technology)_
- Linux _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Visio _(hot technology)_
- Microsoft Word _(hot technology)_
- Guidance Software EnCase Enterprise _(in demand)_
- Automated Biometric Identification System ABIS
- Combined DNA Index System CODIS

## AI exposure & outlook

- **AI task-overlap index:** 57th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 55th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 56th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 63rd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 6th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 12.8% growth (Growing fast); 2.9k annual openings; 20.7k → 23.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $67,440; 19,450 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-19-4092-00_