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Entry Level Environmental Data Scientist Jobs in Reno, NV

... environment - Work shifts longer than 8 hours to support 24/7 operations (covering both night and ... science, network engineering, etc.) - Industry standard certifications (CompTIA, CCENT, Network ...

Entomologist (Army Medical / Scientific Officer)Job Overview As an Entomologist, you'll serve in ... You'll study insects' impact on the health, morale, and operational environment of Soldiers and ...

Entomologist (Army Medical / Scientific Officer)Job Overview As an Entomologist, you'll serve in ... You'll study insects' impact on the health, morale, and operational environment of Soldiers and ...

... data tables, understanding cause and effect relationships, and writing lab reports. Emphasizes hands-on investigation and critical thinking, connecting science to environmental awareness, health, and ...

... environmental data. Ensure safety of navigation, access to denied waters, freedom to maneuver at ... A desired degree is in physics, physics-based oceanography, meteorology, hydrography, earth science ...

... environmental data. Ensure safety of navigation, access to denied waters, freedom to maneuver at ... A desired degree is in physics, physics-based oceanography, meteorology, hydrography, earth science ...

Earth Science Tutor

Reno, NV · Remote

$18 - $40/hr

... data, and understanding watershed dynamics. Emphasizes observational skills and evidence-based reasoning, connecting earth science to natural disaster preparedness, environmental conservation, and ...

Showing results 21-40

Entry Level Environmental Data Scientist information

See Reno, NV salary details

$40.9K

$83.9K

$122.6K

How much do entry level environmental data scientist jobs pay per year?

As of Aug 23, 2026, the average yearly pay for entry level environmental data scientist in Reno, NV is $83,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,800.00 and $98,200.00 per year, depending on experience, location, and employer.

What does an entry level environmental data scientist do?

An Entry Level Environmental Data Scientist collects, analyzes, and interprets environmental data such as air, water, and soil quality. They use statistical methods and programming tools to help identify patterns and trends, supporting research and policy decisions. Often, they work under the guidance of senior scientists to prepare reports, visualize data, and ensure data quality. Their work helps organizations understand environmental impacts and develop solutions for sustainability.

What are the key skills and qualifications needed to thrive as an entry level environmental data scientist?

To thrive as an Entry Level Environmental Data Scientist, you need a background in environmental science or a related field, along with strong skills in data analysis and statistics. Familiarity with programming languages like Python or R, experience with GIS software, and knowledge of data visualization tools are typically required. Analytical thinking, attention to detail, and effective communication are valuable soft skills that help in interpreting and sharing complex findings. These competencies are crucial for generating reliable insights and informing environmental decision-making within organizations.

What types of projects and data sets do entry level environmental data scientists typically work with?

As an entry-level environmental data scientist, you will often work with diverse data sets such as air and water quality measurements, climate records, satellite imagery, and GIS data. Projects can include analyzing trends in pollution, modeling the effects of environmental policies, or creating data visualizations to support sustainability initiatives. You’ll collaborate closely with environmental engineers, researchers, and policy teams, often participating in both data cleaning and preliminary analysis before results are shared with stakeholders. This variety provides valuable exposure to real-world environmental issues and lays a solid foundation for career growth.

What is the difference between Entry Level Environmental Data Scientist vs Entry Level Environmental Analyst?

AspectEntry Level Environmental Data ScientistEntry Level Environmental Analyst
Required CredentialsBachelor's in Environmental Science, Data Science, or related field; some roles may prefer certifications in data analysis or GISBachelor's in Environmental Science, Environmental Management, or related field; certifications in environmental regulations or GIS are common
Work EnvironmentData-focused roles often in labs, research institutions, or corporate sustainability teamsFieldwork, data collection, and reporting in government agencies, consulting firms, or NGOs
Employer & Industry UsageUsed in research, corporate sustainability, and environmental consultingCommon in government agencies, environmental consulting, and non-profit organizations

While both roles require a background in environmental sciences, the Environmental Data Scientist focuses more on data analysis, modeling, and programming, whereas the Environmental Analyst emphasizes data collection, reporting, and regulatory compliance. Understanding these differences helps job seekers target the right roles based on their skills and career goals.

What are the most commonly searched types of Environmental Data Scientist jobs in Reno, NV?

The most popular types of Environmental Data Scientist jobs in Reno, NV are:

What cities near Reno, NV are hiring for Entry Level Environmental Data Scientist jobs?

Cities near Reno, NV with the most Entry Level Environmental Data Scientist job openings:

Bioinformatics Research Scientist - AI Reviewer

micro1 AI

Sparks, NV • Remote

$80 - $110/hr

Part-time

Posted 20 days ago


Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.