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Forestry Data Analyst Jobs (NOW HIRING)

Weyerhaeuser Company

Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

... forests and trees we nurture, we ensure every acre is managed with diligence, patience and pride. That's the Weyerhaeuser way. About the Role Weyerhaeuser's Data & Analytics team is looking for a ...

Weyerhauser Co

Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

... forests and trees we nurture, we ensure every acre is managed with diligence, patience and pride. That's the Weyerhaeuser way. About the Role Weyerhaeuser's Data & Analytics team is looking for a ...

State of Arkansas

FORESTER

De Queen, AR · On-site

$58K - $86K/yr

Prepare and make public presentations and conduct forestry training.Collect data, analyze needs assessments, and prepare written forest management recommendations for private and public forest ...

State of Arkansas

FORESTER

Malvern, AR · On-site

$58K - $86K/yr

Prepare and make public presentations and conduct forestry training.Collect data, analyze needs assessments, and prepare written forest management recommendations for private and public forest ...

Diverse Lynx

Data Mining Manager

Sunrise, FL

Hands-on experience with Python and/or R for data analysis and modeling. * Knowledge of data mining and ML algorithms (e.g., decision trees, random forest, regression, clustering). * Experience with ...

Green Thumb

Data Scientist

Chicago, IL · Hybrid

... forest, decision trees, etc * Ability to communicate analytical findings clearly in writing - you ... Exposure to Snowflake, Snowpark, or cloud data warehouse environments * Experience with dbt or ...

Green Thumb

Data Scientist

Chicago, IL · On-site

... forest, decision trees, etc * Ability to communicate analytical findings clearly in writing - you ... Experience with dbt or working in a layered data warehouse (raw → refined → curated ...

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Forestry Data Analyst information

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$34K

$82.6K

$136K

How much do forestry data analyst jobs pay per year?

As of Jun 14, 2026, the average yearly pay for forestry data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

How does a Forestry Data Analyst typically collaborate with field teams and other departments?

Forestry Data Analysts often work closely with field teams to gather accurate data from forest sites and ensure the integrity of information used in analysis. They regularly communicate findings to forestry managers, ecologists, and GIS specialists to support decision-making in areas like conservation planning and sustainable harvesting. Collaboration also involves participating in cross-departmental meetings and contributing to reports that influence both operational strategies and long-term forest management goals.

What is the difference between Forestry Data Analyst vs Forest Technician?

AspectForestry Data AnalystForest Technician
Required CredentialsBachelor's degree in forestry, environmental science, or related field; data analysis skillsAssociate's degree or technical certification; fieldwork experience
Work EnvironmentOffice-based with field data collection; data analysis and reportingPrimarily in the field; data collection and site assessments
Employer & Industry UsageGovernment agencies, research institutions, consulting firmsForestry services, conservation agencies, government departments
Common Search & ComparisonData analysis, GIS, forest managementFieldwork, forest surveys, data collection

The Forestry Data Analyst focuses on analyzing forest data, creating reports, and supporting management decisions using data analysis tools. In contrast, the Forest Technician primarily conducts fieldwork, collects data on forest conditions, and supports field surveys. Both roles are essential in forestry but differ mainly in their focus—analytical versus fieldwork tasks.

What are the key skills and qualifications needed to thrive as a Forestry Data Analyst, and why are they important?

To thrive as a Forestry Data Analyst, you need strong analytical skills, a background in environmental science or forestry, and proficiency in statistical analysis. Experience with GIS software, remote sensing tools, and data management systems—along with certifications like GIS Professional (GISP)—are typically required. Excellent problem-solving abilities, attention to detail, and effective communication skills help distinguish top performers in this role. These competencies enable accurate data interpretation and support informed decision-making for sustainable forest management.

What does a Forestry Data Analyst do?

A Forestry Data Analyst collects, processes, and interprets data related to forests, such as tree growth, forest health, and resource usage. They use statistical and geospatial analysis tools to generate insights that help guide forest management and conservation efforts. Their work supports decision-making for sustainable forestry practices and can involve collaborating with scientists, government agencies, and environmental organizations. Typical tasks include data cleaning, GIS mapping, and preparing reports or visualizations to communicate findings.
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What cities are hiring for Forestry Data Analyst jobs? Cities with the most Forestry Data Analyst job openings:
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Forestry Data Analyst jobs near you
Infographic showing various Forestry Data Analyst job openings in the United States as of June 2026, with employment types broken down into 31% Full Time, 38% Part Time, and 31% Contract. Highlights an 81% Physical, 8% Hybrid, and 11% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.
Scientific Analyst II

Scientific Analyst II

University of Arizona

Tucson, AZ

Other

Posted 24 days ago

University Of Arizona rating

7.1

Company rating: 7.1 out of 10

Based on 66 frontline employees who took The Breakroom Quiz

350th of 537 rated colleges and universities

Job description

Data Analysis and Machine Learning Pipeline Development:

  • Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
  • Apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of menopausal hormone therapy (MHT) on brain health.
  • Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

  • Collaborating in computational drug repurposing analyses to identify existing FDA-approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment. Integrate multi-omics data (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
  • Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

  • Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
  • Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
  • Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
  • Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

  • Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
  • Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
  • Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough documentation of analytical methods to ensure transparency and reproducibility.
  • Participate in lab meetings, journal clubs, and professional development activities.

Research Infrastructure and Continuous Improvement:

  • Maintain and improve lab computational infrastructure, including code repositories (GitHub), analytical workflows, and documentation standards.
  • Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
  • Assist in training junior lab members or graduate students on data science methods and tools as needed.
  • Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

  • Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
  • Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
  • Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Knowledge of drug repurposing methodologies or network pharmacology.
  • Knowledge and familiarity with electronic medical records data analysis.
  • Knowledge and proficiency with SQL and database management.
  • Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
  • Ability to manage multiple concurrent projects and meet deadlines.
  • Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
  • Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

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