# Environmental Restoration Planners

> Collaborate with field and biology staff to oversee the implementation of restoration projects and to develop new products. Process and synthesize complex scientific data into practical strategies for restoration, monitoring or management.

- **SOC code:** 19-2041.02
- **Canonical URL:** https://singulariki.com/roles/role-19-2041-02
- **Also known as:** Coastal and Estuary Specialist, Habitat Restoration Specialist, Marine Habitat Resources Specialist, Restoration Ecologist, Fisheries Habitat Restoration Specialist, Restoration Specialist, Watershed Coordinator, Conservation Planner
- **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):
- Develop environmental restoration project schedules and budgets.
- Provide technical direction on environmental planning to energy engineers, biologists, geologists, or other professionals working to develop restoration plans or strategies.
- Create habitat management or restoration plans, such as native tree restoration and weed control.
- Conduct site assessments to certify a habitat or to ascertain environmental damage or restoration needs.
- Collect and analyze data to determine environmental conditions and restoration needs.
- Supervise and provide technical guidance, training, or assistance to employees working in the field to restore habitats.
- Plan environmental restoration projects, using biological databases, environmental strategies, and planning software.
- Communicate findings of environmental studies or proposals for environmental remediation to other restoration professionals.
- Apply for permits required for the implementation of environmental remediation projects.
- Inspect active remediation sites to ensure compliance with environmental or safety policies, standards, or regulations.
- Develop natural resource management plans, using knowledge of environmental planning or state and federal environmental regulatory requirements.
- Identify environmental mitigation alternatives, ensuring compliance with applicable standards, laws, or regulations.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Biology _(knowledge)_
- Critical Thinking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Written Expression _(ability)_
- Inductive Reasoning _(ability)_
- English Language _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Writing _(essential_skill)_
- Speaking _(essential_skill)_

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

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Adobe Acrobat _(hot technology)_
- Adobe Creative Cloud software _(hot technology)_
- Adobe Illustrator _(hot technology)_
- Adobe InDesign _(hot technology)_
- Adobe Photoshop _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Autodesk AutoCAD Civil 3D _(hot technology)_
- ESRI ArcGIS software _(hot technology)_
- Microsoft Access _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 75th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 75th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 87th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 60th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 20th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 4.4% growth (About average); 8.5k annual openings; 90.3k → 94.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $80,060; 84,930 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-19-2041-02_