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Tree Tagging Jobs in Washington (NOW HIRING)

JS Consulting

Data Scientist

Washington, DC · On-site

... tagging, Name Entity Recognition (NER), Bag of Words, text extraction * Experience building and ... Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement ...

Gromelski & Associates

Network Architect

Manassas, VA · On-site

$135K - $200K/yr

... tree, routing (static and dynamically implemented protocols), ACLs, IP addressing and subnetting ... 1s), DHCP, 802.1q tagging, multicast, PAgP or LACP (EtherChannel), QoS and Active-Active ...

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Tree Tagging information

What is tree tagging?

Tree tagging is the process of identifying and labeling individual trees, often by attaching a tag or marker to each one. This practice is commonly used in forestry, research, urban planning, and conservation to keep track of tree species, monitor health, or record data for inventories. The tags typically include unique identification numbers or codes, and sometimes additional information like species name or planting date. Tree tagging helps organizations manage tree populations more effectively and supports efforts in biodiversity tracking and environmental studies.

What are the key skills and qualifications needed to thrive as a tree tagging technician?

To thrive as a Tree Tagging Technician, you typically need knowledge of dendrology, attention to detail, and experience with field data collection, often supported by a degree or coursework in forestry or environmental science. Familiarity with GPS devices, data entry software, and sometimes GIS mapping tools is commonly required. Strong observational skills, physical stamina, and effective communication help technicians operate efficiently in various outdoor environments and collaborate with teams. These skills are crucial for ensuring accurate tree identification, data integrity, and the success of ecological or forestry projects.

What are the typical challenges faced while performing tree tagging in the field?

Tree tagging professionals often encounter challenges such as difficult terrain, varying weather conditions, and ensuring accurate identification and documentation of tree species. Working outdoors means adapting to environmental changes and sometimes navigating dense vegetation or remote areas. Additionally, effective communication with team members and adherence to safety protocols are essential to ensure efficient and accurate tagging while minimizing risks.

What is the difference between Tree Tagging vs Arborist?

AspectTree TaggingArborist
CredentialsTypically requires basic knowledge, safety trainingRequires certifications like ISA Arborist Certification
Work EnvironmentFieldwork involving tagging trees in parks, urban areasFieldwork including pruning, planting, diagnosing tree health
Industry UsageUsed mainly for identification and inventoryInvolved in tree care, maintenance, and health management
Search IntentComparing tree identification methodsLooking for professional tree care services or careers

Tree Tagging focuses on labeling trees for identification and inventory purposes, often requiring minimal certification. An Arborist, on the other hand, is a trained professional who provides comprehensive tree care, including pruning, health assessment, and safety. While both roles work outdoors and in the same industry, their responsibilities and credentials differ significantly.

What are popular job titles related to Tree Tagging jobs in Washington?

For Tree Tagging jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Tree Tagging jobs in Washington look for?

The top searched job categories for Tree Tagging jobs in Washington are:

What cities in Washington are hiring for Tree Tagging jobs?

Cities in Washington with the most Tree Tagging job openings:

Tree Tagging jobs near you
Infographic showing various Tree Tagging job openings in Washington as of September 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution.

Data Scientist

Washington, DC • On-site

JS Consulting
Custom Software Development Services • 1 - 10 employees
Learn more about JS Consultingexternal

Contractor

Re-posted 29 days ago

Job description

Job Title- Data Scientist

Project Location – Onsite in Washington, District of Columbia

Duration- 6+ months contract

Visa- USC

Must have PHD

 Minimum Qualifications:

  • Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.
  • Demonstrated experience programming with R/Python, Linux, and Spark in AWS cloud environment, or knowledge and algorithmic design experience in Python (3+ years)
  • Proficient with Amazon AWS Sagemaker, Jupyter Notebook and Python Scikit, Deep Learning, Machine Learning tools such as TensorFlow
  • Experience with image processing models such as Coco, CLIP, ResNet or comparable models
  • Demonstrated experience with machine learning techniques including natural language processing, and Large language Models (GPTv4-o1, o3, OpenAI APIs, Llama, Claude, etc).
  • Experience developing AI agents and development proficiency using agentic programming
  • Proficient in Natural language processing (NLP) and Natural language generation (NLG) including prior projects in any of the following categories: top modeling of text, sentiment analysis of text, part of speech tagging, Name Entity Recognition (NER), Bag of Words, text extraction
  • Experience building and working with any of these components: Vector DB, BERT, RoBERTa (or comparable tools), Spacy, LLM and GenAI tools. Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs.
  • Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITL
  • Demonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.
  • Demonstrated experience processing structured and unstructured data sources, data cleansing, data normalization and prep for analysis
  • Demonstrated experience with code repositories and build/deployment pipelines, specifically Jenkins and/or Git/GitHub/GitLab.
  • Demonstrated experience using Tableau, or Kibana, Quicksights or other similar data visualizations tools.
  • Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)

 Qualifications & Requirements

  • Education: MS in Computer Science, Statistics, Math, Engineering, or related field, PhD required.
  • 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems
  • 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM)
  • 1+ year of experience building NLP and NLG tools.
  • Experience with wide range of LLMs (Llama, Claude, OpenAI, Cohere, etc.), LoRA, LangChain, RAG, LLM Fine Tuning and PEFT are preferred.
  • Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
  • Passion for solving complex data problems and generating cross-functional solutions in a fast-paced environment
  • Knowledge in Python and SQL, object oriented programming, service oriented architectures
  • Strong scripting skills with Shell script and SQL
  • Strong coding skills and experience with Python (including SciPy, NumPy, and/or PySpark) and/or Scala.
  • Knowledge and implementation experience with NLP techniques (topic modeling, bag of words, text classification, TF/IDF, Sentiment analysis) and NLP technologies such as Python NLTK, or Spacy or comparable technologies
  • Knowledge and implementation experience with statistical and machine learning models (regression, classification, clustering, graph models, etc.)

 Preferred Qualifications

  • Hands on experience building models with deep learning frameworks like Tensorflow, Keras, Caffe, PyTorch, Theano, H2O, or similar
  • Experience with LLM Agents, Agentic programming
  • Experience with search architecture (for instance: Solr, ElasticSearch, AWS OpenSearch)
  • Experience with building querying ontologies such as Zeno, OWL, RDF, SparQL or comparable are preferred
  • Knowledge & experience with microservices, service mesh, API development and test automation are preferred
  • Demonstrated experience using Docker, Kubernetes, and/or other similar container frameworks are preferred

 Additional Job Qualifications:

  • Ability to translate business ideas into analytics models that have major business impact.
  • Demonstrated experience working with multiple stakeholders.
  • Demonstrated communication skills, e.g. explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats.
  • Demonstrated experience developing tested, reusable and reproducible work.