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Remote Environmental Science Jobs in Phoenix, AZ

Mondo

Data Scientist

Phoenix, AZ · Remote

$65 - $75/hr

Remote (Candidate must reside in Pacific, Mountain, or Central time zone. Eastern time zone and ... enterprise environments * Build and scale machine learning and AI models on cloud platforms ...

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All JobsEnvironmental Science JobsRemote Environmental Science JobsRemote Environmental Science Jobs in Phoenix, AZ

Remote Environmental Science information

See Phoenix, AZ salary details

$40.7K

$83.5K

$122.1K

How much do remote environmental science jobs pay per year?

As of Aug 16, 2026, the average yearly pay for remote environmental science in Phoenix, AZ is $83,527.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,500.00 and $97,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote environmental scientist, and why are they important?

To thrive as a Remote Environmental Scientist, you need a solid background in environmental science, data analysis, and report writing, usually supported by a relevant degree. Familiarity with GIS software, remote sensing tools, and environmental modeling systems is typically required, along with certifications like GIS Professional (GISP) or LEED accreditation. Strong communication, problem-solving, and self-management skills are crucial for collaborating with remote teams and stakeholders. These competencies enable effective environmental assessments, data-driven decision-making, and successful project outcomes from a remote work environment.

What is a remote environmental science job?

A remote environmental science job is a position that allows professionals to work from locations outside of a traditional office or laboratory setting, often from home or while traveling. These roles typically involve tasks such as data analysis, report writing, remote sensing, consulting, and environmental monitoring using digital tools. Remote environmental scientists contribute to research, policy, and project management without needing to be physically present at field sites, although occasional site visits may be required. This flexibility allows for better work-life balance and can open opportunities to collaborate with global teams. Remote roles are increasingly common as technology enables more scientific work to be conducted virtually.

How do remote environmental science professionals typically collaborate with field teams and other stakeholders?

Remote environmental science professionals often rely on digital communication tools, such as video conferencing, cloud-based data sharing, and collaborative project management platforms, to stay connected with field teams and stakeholders. They may participate in regular virtual meetings to discuss project updates, analyze collected data, and coordinate research activities. Effective communication and strong organizational skills are essential to ensure alignment and successful project outcomes, even when team members are dispersed across different locations. Building strong professional relationships remotely and staying proactive in communication helps overcome challenges associated with working outside a traditional office or field setting.

What is the difference between Remote Environmental Science vs Remote Environmental Technician?

AspectRemote Environmental ScienceRemote Environmental Technician
Required CredentialsBachelor's or higher in environmental science or related field; certifications varyAssociate's or bachelor's in environmental technology or related field; certifications may include safety or technical training
Work EnvironmentPrimarily office-based or remote; fieldwork may be occasionalMostly remote with some field site visits or technical tasks
Employer & Industry UsageEnvironmental consulting firms, government agencies, research institutionsEnvironmental service companies, government agencies, industrial firms
Common Search & ComparisonRemote Environmental ScienceRemote Environmental Technician

Remote Environmental Science roles focus on research, data analysis, and environmental planning, often requiring higher education and offering more analytical responsibilities. In contrast, Remote Environmental Technicians handle technical tasks, site assessments, and field data collection, typically with technical certifications. Both roles are vital in environmental projects but differ in scope, credentials, and daily tasks.

What are the most commonly searched types of Environmental Science jobs in Phoenix, AZ?

The most popular types of Environmental Science jobs in Phoenix, AZ are:

What are popular job titles related to Remote Environmental Science jobs in Phoenix, AZ?

For Remote Environmental Science jobs in Phoenix, AZ, the most frequently searched job titles are:

What job categories do people searching Remote Environmental Science jobs in Phoenix, AZ look for?

The top searched job categories for Remote Environmental Science jobs in Phoenix, AZ are:

What cities near Phoenix, AZ are hiring for Remote Environmental Science jobs?

Cities near Phoenix, AZ with the most Remote Environmental Science job openings:

Remote Environmental Science jobs near you
Infographic showing various Remote Environmental Science job openings in Phoenix, AZ as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 77% Full Time, 16% Part Time, and 5% Contract. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution, with an average salary of $83,527 per year, or $40.2 per hour.

Data Scientist

Mondo

Phoenix, AZ • Remote

$65 - $75/hr

Contractor

Medical, Dental, Vision, Retirement

Re-posted 2 days ago

Job description

Apply now: Senior Data Scientist , Remote. Start date is ASAP for this 12 Month Contract position.

Job Title: Senior Data ScientistLocation/Type: Remote (Candidate must reside in Pacific, Mountain, or Central time zone. Eastern time zone and international candidates will not be considered.)Start Date: ASAPDuration: Contract, 6  Months (extension likely)Compensation Range: $65/hr to $75/hrBenefits: Eligible for Health, Dental, Vision, and 401KVisa Sponsorship: Not eligible for visa sponsorship

Job Description:The client is seeking a Data Scientist with deep expertise in Generative AI, agentic architectures, and MLOps to design, build, and scale end to end AI solutions while embedding Responsible AI practices across the full development lifecycle. This role requires hands on MLOps maturity, not just model building, the candidate will own how models move from experimentation into production and stay reliable once they get there.

Job Summary:

  • Design and deploy end to end RAG solutions and autonomous AI agents in cloud and enterprise environments
  • Build and scale machine learning and AI models on cloud platforms, primarily AWS or Azure
  • Develop and maintain MLOps pipelines to support model deployment, monitoring, versioning, and governance
  • Own CI/CD for ML workflows, including automated retraining, model registry management, and rollback procedures
  • Implement model monitoring for drift, performance degradation, and data quality issues in production
  • Apply statistical modeling techniques to solve complex business problems
  • Collaborate with stakeholders across the organization to translate requirements into scalable AI solutions
  • Embed Responsible AI practices across model development, deployment, and governance workflows
  • Contribute across the full development lifecycle, from experimentation through production release

Requirements:

Must Haves:

  • Location: candidate must be based in Pacific, Mountain, or Central time zone. This is a hard requirement, not a preference.
  • Minimum 4 years of experience working specifically as a Data Scientist (title and scope must match, not adjacent titles like Data Analyst or ML Engineer alone)
  • Must currently or most recently hold a Data Scientist title (Data Scientist, Senior Data Scientist, Staff Data Scientist, Principal Data Scientist, etc.). Candidates whose current or most recent role carries a different title (Data Analyst, ML Engineer, Analytics Engineer, etc.)
  • Minimum 3 years of hands on MLOps experience, specifically model deployment, monitoring, and lifecycle management in production environments (not just model development or notebooks)
  • Direct experience with at least one MLOps tooling stack such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines
  • Master's degree in a STEM field
  • 4 years of proficiency in SQL
  • 4 years of proficiency in Python
  • Hands on experience with AWS or Azure cloud platforms
  • Proficiency with Git for version control
  • Strong communication skills with demonstrated ability to work cross functionally with stakeholders

Nice to Haves:

  • Experience with Snowflake for data warehousing and analytics
  • Hands on experience with AWS specifically, in addition to general cloud proficiency
  • Startup or fast paced environment mindset with comfort navigating ambiguity
  • Active personal use of AI tools and familiarity with the evolving AI landscape