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Climate Research Scientist Machine Learning Jobs

MIT Human Resources

Research Scientist 3

Cambridge, MA · On-site

... science, machine learning, use of machine learning for enhancing mathematical discovery and formal verification; help set the project's research direction, lead several of the program's parallel ...

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Climate Research Scientist Machine Learning information

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$50.5K

$130.1K

$174K

How much do climate research scientist machine learning jobs pay per year?

As of Sep 11, 2026, the average yearly pay for climate research scientist machine learning in the United States is $130,117.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What does a climate research scientist specializing in machine learning do?

A Climate Research Scientist who specializes in Machine Learning uses advanced algorithms and computational models to analyze climate data and improve predictions about climate change. They work with large datasets from satellites, weather stations, and simulations to identify patterns, make forecasts, and assess environmental impacts. Their work helps inform policy decisions, guide mitigation strategies, and advance our scientific understanding of the Earth's climate system. Collaboration with other scientists, governments, and organizations is often a key part of the role.

What are the key skills and qualifications needed to thrive as a climate research scientist specializing in machine learning?

To thrive as a Climate Research Scientist specializing in Machine Learning, you need a solid background in climate science, statistical analysis, and advanced machine learning techniques, typically supported by a graduate degree in a related field. Experience with programming languages like Python or R, familiarity with climate modeling software, and proficiency in machine learning frameworks such as TensorFlow or PyTorch are highly valuable. Strong analytical thinking, problem-solving abilities, and effective communication skills help you explain complex findings to diverse audiences and collaborate across disciplines. These skills and qualities are crucial for advancing climate research, developing innovative solutions, and informing policy decisions based on robust data analysis.

How do climate research scientists specializing in machine learning typically collaborate with multidisciplinary teams?

Climate Research Scientists with expertise in Machine Learning often work closely with meteorologists, data engineers, environmental scientists, and policy experts. They contribute by developing and refining predictive models using large climate datasets, while also translating complex outputs into actionable insights for decision-makers. Collaboration often involves regular team meetings, joint publications, and integrating domain expertise to ensure that the models are both scientifically robust and practically useful. Strong communication skills are valuable, as these scientists frequently explain technical concepts to colleagues from non-technical backgrounds.

What is the difference between Climate Research Scientist Machine Learning vs Climate Data Analyst?

AspectClimate Research Scientist Machine LearningClimate Data Analyst
Required CredentialsMaster's or PhD in Climate Science, Data Science, or related fields; knowledge of machine learningBachelor's or Master's in Environmental Science, Data Analysis, or related fields; proficiency in data tools
Work EnvironmentResearch labs, universities, environmental agencies, often collaborative and interdisciplinaryGovernment agencies, consulting firms, NGOs; focus on data processing and reporting
Employer & Industry UsageResearch institutions, academia, environmental organizations integrating machine learningPolicy organizations, environmental consultancies analyzing climate data

While both roles involve climate data, Climate Research Scientist Machine Learning focuses on developing predictive models using advanced algorithms, whereas Climate Data Analysts primarily process and interpret climate datasets to inform decisions. The former requires more specialized knowledge in machine learning techniques, while the latter emphasizes data management and reporting skills.

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Climate Research Scientist Machine Learning jobs near you
Infographic showing various Climate Research Scientist Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $130,117 per year, or $62.6 per hour.

Data Scientist / Machine Learning Engineer

Arlington, VA • On-site

Electronic Consulting Services, Inc (ECS Federal)
See more jobs at Electronic Consulting Services, Inc (ECS Federal)external

$160K - $185K/yr

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Job Description
Everforth ECS is seeking a Data Scientist/Machine Learning Engineer to join our team in Arlington, VA (Hybrid) . This position is contingent upon award.
We are seeking a talented Data Scientist / Machine Learning Engineer to design, develop, deploy, and optimize advanced analytics and machine learning solutions that drive business insights and operational efficiencies. This role combines data science, machine learning engineering, and software development to transform complex data into scalable, production-ready AI and predictive analytics solutions.
The ideal candidate will possess expertise in statistical analysis, machine learning algorithms, AI/ML-assisted clustering, feature engineering, model deployment, anomaly detection, and cloud-based AI platforms while collaborating closely with business stakeholders, data engineers, and technology teams.
Key Responsibilities
Data Science & Advanced Analytics
  • Analyze structured and unstructured data to identify trends, patterns, and actionable insights.
  • Develop predictive, prescriptive, and classification models to support business objectives.
  • Perform exploratory data analysis (EDA), feature engineering, and statistical modeling.
  • Design experiments and evaluate model performance using appropriate statistical methodologies.
  • Present findings and recommendations to technical and non-technical stakeholders.
  • Support efforts in anomaly detection.
Machine Learning Development
  • Design, build, train, and optimize machine learning and deep learning models.
  • Develop solutions for forecasting, anomaly detection, natural language processing (NLP), recommendation systems, and computer vision applications.
  • Evaluate and select appropriate algorithms based on business requirements and performance objectives.
  • Continuously improve model accuracy, scalability, and maintainability.
MLOps & Production Engineering
  • Deploy machine learning models into production environments.
  • Build automated model training, validation, deployment, and monitoring pipelines.
  • Implement CI/CD practices for machine learning workflows.
  • Support AI/ML-assisted clustering efforts.
  • Monitor model performance and address model drift, data drift, and operational issues.
  • Maintain model governance, versioning, and documentation standards.
Data Engineering & Platform Integration
  • Collaborate with data engineers to develop scalable data pipelines and feature stores.
  • Integrate machine learning solutions into enterprise applications and business processes.
  • Optimize data processing workflows for large-scale datasets.
  • Ensure data quality, security, and compliance standards are maintained.
Cloud & AI Platforms
  • Develop and deploy solutions using cloud-native AI and machine learning services.
  • Leverage platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Vertex AI, or equivalent technologies.
  • Perform multi-source summarization with human-review workflow by combining AI-driven aggregation of diverse sources with targeted human validation.
  • Utilize distributed computing frameworks to support large-scale analytics workloads.
  • Support enterprise AI strategy and modernization initiatives.
Collaboration & Innovation
  • Partner with business leaders to identify opportunities for AI and advanced analytics solutions.
  • Translate business requirements into machine learning use cases and technical requirements.
  • Stay current on emerging technologies, AI trends, and industry best practices.
  • Contribute to innovation initiatives, proofs of concept, and research activities.
Salary Range: $160,000 - $185,000
General Description of Benefits
Required Skills
  • Top Secret Clearance
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
  • 5+ years of experience in Data Science, Machine Learning Engineering, Artificial Intelligence, or Advanced Analytics.
  • Strong understanding of machine learning algorithms, statistical analysis, and data modeling techniques.
  • Experience building and deploying machine learning models in production environments.
  • Proficiency in Python and machine learning libraries/frameworks.
  • Strong knowledge of SQL and data manipulation techniques.
  • Experience working with large datasets and cloud-based data platforms.
  • Excellent problem-solving, analytical, and communication skills.

Desired Skills
  • Master's degree in Data Science, Machine Learning, Artificial Intelligence, Statistics, or related discipline.
  • Experience with Generative AI and Large Language Models (LLMs).
  • Familiarity with Retrieval-Augmented Generation (RAG) architectures.
  • Experience supporting enterprise AI transformation initiatives.
  • Knowledge of model governance, responsible AI, and AI risk management frameworks.
  • Experience working in highly regulated industries such as healthcare, finance, government, or defense.
#EverforthECS1
ECS Federal LLC is an equal opportunity employer and does not discriminate or allow discrimination on the basis any characteristic protected by law. All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, or local jurisdiction law.
Everforth ECS is the federal segment of Everforth , a $4B global organization with over 10,000 employees. Our nearly 3,500 professionals deliver advanced technology solutions in data and AI, cybersecurity, and enterprise transformation, serving defense, intelligence, and federal civilian agencies.
Our work powers mission-critical outcomes, strengthens technology partnerships, and creates meaningful opportunities for our people. We are defined by a commitment to excellence in delivery, a culture of innovation, and an environment where talent can thrive and grow.
We value:
  • Attracting and developing top talent and high-performing teams
  • Fostering a culture that is engaging, accountable, and mission-driven

Meet the challenge. Make a difference with Everforth ECS!

Apply