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Climate Research Scientist Machine Learning Jobs in Saco, ME

... research, or applied project experience in data science or machine learning. • Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design. • Hands ...

Data Scientist

Portland, ME · On-site

$87K - $123K/yr

... or machine learning. * Experience gained through internships, co-ops, academic research, or applied capstone projects is acceptable. * Industry experience is preferred. Knowledge, Skills, and ...

... machine learning. * Experience gained through internships, co-ops, academic research, or applied capstone projects is acceptable. * Industry experience is preferred. Knowledge, Skills, and ...

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Showing results 21-40

Climate Research Scientist Machine Learning information

See Saco, ME salary details

$52.9K

$136.2K

$182.2K

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

As of Sep 8, 2026, the average yearly pay for climate research scientist machine learning in Saco, ME is $136,238.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,600.00 and $181,100.00 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.

What cities near Saco, ME are hiring for Climate Research Scientist Machine Learning jobs?

Cities near Saco, ME with the most Climate Research Scientist Machine Learning job openings:

Full-time

Re-posted 15 days ago


Job description

Job Summary:
Northeastern University is seeking a Data Scientist for its AI Solutions Hub at the Roux Institute in Portland, Maine. This role is designed for early-career data scientists to contribute to the development and delivery of AI and data science solutions across various industries while gaining exposure to production systems and modern AI practices.
Responsibilities:
• Perform data cleaning, exploratory data analysis (EDA), and feature engineering.
• Train, evaluate, and compare machine learning models under supervision.
• Assist with model validation, performance monitoring, and documentation.
• Contribute to ML pipelines and collaborate with ML engineers on deployment-related tasks.
Qualifications:
Required:
• Master’s degree in Computer Science, Engineering, Applied Mathematics, Statistics, or a closely related field.
• 0–2 years of industry, research, or applied project experience in data science or machine learning.
• Solid understanding of statistical methods, regression, hypothesis testing, and basic experimental design.
• Hands-on experience with classical machine learning methods such as linear/logistic regression, decision trees, and gradient boosting.
• Familiarity with deep learning concepts and modern architectures (e.g., convolutional neural networks or transformers); deep specialization is not required.
• Proficiency in Python for data analysis and model development (NumPy, pandas, scikit-learn).
• Working knowledge of SQL and relational databases.
• Familiarity with at least one ML or deep learning framework (e.g., PyTorch, TensorFlow, HuggingFace).
• Ability to clearly communicate analytical findings to technical and non-technical audiences with guidance.
• Collaborate effectively with cross-functional teams including data scientists, engineers, project managers, and faculty experts.
• Willingness to participate in client meetings in a supporting role.
• Awareness of ethical AI principles including fairness, transparency, and responsible model use.
• Willingness to follow established governance, documentation, and review practices.
• Strong curiosity and motivation to learn new tools, techniques, and AI methods.
• Openness to feedback and mentorship.
• Ability to manage assigned tasks, meet deadlines, and maintain high-quality work.
• Proactive attitude and willingness to take increasing responsibility over time.
Preferred:
• Exposure to NLP, computer vision, or speech processing through coursework or academic/industry projects.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Understanding of software development best practices such as version control (Git) and Agile workflows.
Company:
Founded in 1898, Northeastern is a global research university with a distinctive, experience-driven approach to education and discovery. Founded in 1898, the company is headquartered in Boston, USA, with a team of 5001-10000 employees. The company is currently Late Stage.