2

Quantitative Ecology Remote Jobs (NOW HIRING)

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... of experimental ecology, pollinator physiology and quantitative analysis to address questions ...

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... The candidate should also have strong quantitative and computational skills, knowledge of R, python ...

Quantitative Ecology Remote information

See salary details

$98K

$169.7K

$259.5K

How much do quantitative ecology remote jobs pay per year?

As of Jun 9, 2026, the average yearly pay for quantitative ecology remote in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

How does a Quantitative Ecology role typically interact with interdisciplinary teams in a remote setting?

In a remote Quantitative Ecology position, collaboration with interdisciplinary teams is essential, as projects often require expertise from biology, statistics, data science, and environmental policy. Communication is usually facilitated through virtual meetings, collaborative platforms, and shared data repositories, ensuring seamless integration of diverse skill sets. Regular coordination with team members helps in aligning research objectives, troubleshooting data issues, and synthesizing findings for broader ecological insights. Remote work also demands strong organizational skills and proactive communication to maintain project momentum and foster effective teamwork.

What is quantitative ecology?

Quantitative ecology is a branch of ecology that uses mathematical, statistical, and computational tools to analyze ecological data and address ecological questions. Quantitative ecologists develop and apply models to study patterns and processes in the natural world, such as species distribution, population dynamics, and ecosystem function. This field is essential for making informed decisions in conservation, resource management, and environmental policy, especially as ecological data becomes increasingly complex and abundant. Remote quantitative ecologists often use programming, data analysis, and modeling techniques to work with large datasets from various locations.

What are the key skills and qualifications needed to thrive as a Quantitative Ecologist working remotely, and why are they important?

To thrive as a Quantitative Ecologist working remotely, you need a strong background in ecology, statistics, and data analysis, often supported by an advanced degree in ecology, environmental science, or a related field. Familiarity with statistical software (such as R or Python), GIS tools, and remote collaboration platforms is typically required. Excellent problem-solving, self-motivation, and communication skills help you interpret complex data and work effectively with dispersed teams. These skills are crucial for producing rigorous scientific insights and effectively contributing to research projects from a remote environment.
More about Quantitative Ecology Remote jobs
What cities are hiring for Quantitative Ecology Remote jobs? Cities with the most Quantitative Ecology Remote job openings:
What are the most commonly searched types of Quantitative Ecology jobs? The most popular types of Quantitative Ecology jobs are:
What states have the most Quantitative Ecology Remote jobs? States with the most job openings for Quantitative Ecology Remote jobs include:
Infographic showing various Quantitative Ecology Remote job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.
Postdoctoral Associate - Forest Futures Lab/Western Fire and Forest Resilience Collaborative

Postdoctoral Associate - Forest Futures Lab/Western Fire and Forest Resilience Collaborative

Cary Institute of Ecosystem Studies

Millbrook, NY โ€ข Remote

Full-time

Posted 14 days ago


Job description

Salary transparency statement This is a full-time, fully benefitted exempt position with a one-year initial appointment, renewable for an additional year contingent on performance and funding. Annual salary starts at $74,263.00 , based on postdoctoral experience, with a highly competitive benefits and time-off package.

Position Summary

The Cary Institute of Ecosystem Studies seeks a collaborative, synthetic postdoctoral associate to join the Western Fire and Forest Resilience Collaborative and the Forest Futures Lab. The Fire Collaborative includes 12 science teams and a team of boundary spanners working together to advance predictive science in fire ecology and ecosystem resilience in ways that are actionable for western US land managers. The postdoctoral associate will integrate remote sensing and geospatial data to determine how fire regimes affect western US river runoff and the drivers of fire-water relationships. This role offers leadership opportunities and the freedom to pursue independent research within a supportive and nurturing lab environment.

The position is based at the Cary Institute in the beautiful Hudson Valley of New York, a short way north of New York City. The Cary Institute is home to a diverse, vibrant, and supportive community of colleagues. The anticipated start date is on or around January 4th, 2027.

Questions about the position may be directed to Dr. Winslow Hansen at the Cary Institute.

Essential Responsibilities

Present research findings in peer-reviewed papers, at scientific meetings, and in other forums.

Work with colleagues in the Fire Collaborative to develop, lead, and implement independent research that enhances fire ecology and ecosystem resilience strategies.

Leverage remote sensing and geospatial data in AI and Bayesian analytical frameworks to characterize relationships between fire and subsequent river runoff in western US watersheds.

Engage with Collaborative boundary spanners, scientists, policymakers, and stakeholders to ensure research findings effectively inform fire and forest management strategies.

Contribute to a dynamic and interdisciplinary research environment at the Cary Institute.

Required Qualifications

Ph.D. in forest ecology, disturbance ecology, remote sensing, AI, or a related field prior to appointment.

Demonstrated expertise in Earth observation, ecological remote sensing, fire ecology.

Experience working with western US practitioners for producing actionable science.

Strong quantitative and analytical skills, particularly with Bayesian and AI-based analyses.

Ability to work independently.

Proficiency with Google Earth Engine.

Strong written and oral communication skills.

Proficiency in programming languages such as R, Python, Java script.

Preferred Qualifications (Not required in every job description only as needed)

Past postdoctoral experience.

Proven record of successful collaboration in team-science environments.

Past experience linking terrestrial disturbances with outcomes in adjacent freshwater systems.

Past experience using remote sensing to characterize ecological disturbance in western landscapes.

Working Conditions

Full-time, Exempt, fully benefitted position.

Occasional travel for field work or meetings is required.

Will require computer work for 7 hours or more a day.

Closing Date: Review of applications will begin 05/19/2026

To Apply:

Visit our website at https://www.caryinstitute.org/... and complete our online job application. Please submit one document that includes a cover letter (no more than 2 pages) describing research interests, CV, and contact information for three references. The Cary Institute is an Equal Employment Opportunity (EEO) employer. It is the policy of the Institute to provide equal employment opportunities to all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, familial status, protected veteran or disabled status, or genetic information.

Employment Type: FULL_TIME