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Entry Level Data Science Renewable Energy Jobs (NOW HIRING)

General Atomics

Data Scientist I

San Diego, CA · On-site

We currently have an exciting opportunity for an entry level Data Scientist within our IFT Division located in San Diego, California. DUTIES AND RESPONSIBILITIES: * Own the design, development, and ...

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ControlPoint Technologies

Entry Level Engineer

Rockland, MA · On-site

$68K - $93K/yr

Projects include renewable energy interconnection, utility system planning, substation engineering ... Bachelor of Science or Master of Science Engineering Degree from ABET accredited program/university.

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How much do entry level data science renewable energy jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for entry level data science renewable energy in the United States is $20.24, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $21.88 per hour, depending on experience, location, and employer.

What is an entry level data scientist in renewable energy?

Entry level data science jobs in renewable energy involve analyzing and interpreting data to optimize the generation, distribution, and consumption of renewable energy sources like solar, wind, and hydro power. These roles typically include tasks such as cleaning and processing data, building predictive models, visualizing trends, and supporting decision-making with actionable insights. Professionals in this field often work with large datasets from sensors, weather data, and energy grids to help companies increase efficiency, reduce costs, and support sustainability goals.

What does an entry level data scientist do in renewable energy?

As an entry-level data scientist in the renewable energy sector, you’ll typically analyze large datasets from sources like solar panels, wind turbines, or energy grids to identify trends, optimize performance, and predict energy output. You may also support the development of machine learning models for forecasting demand, detecting equipment anomalies, or improving energy efficiency. Collaboration with engineers, project managers, and other data professionals is common, allowing you to learn cross-functional skills while contributing to impactful sustainability initiatives.

What skills and qualifications are needed to thrive as an entry level data scientist in renewable energy?

To succeed as an Entry Level Data Scientist in Renewable Energy, you need a strong background in statistics, data analysis, and programming (often with a degree in a quantitative field). Familiarity with tools like Python, R, SQL, machine learning libraries, and data visualization platforms is typically required. Strong problem-solving, communication skills, and a collaborative mindset help you interpret results and work effectively with cross-functional teams. These abilities enable you to derive actionable insights from complex data, driving innovation and efficiency in the renewable energy sector.

What is the difference between Entry Level Data Science Renewable Energy vs Entry Level Data Analysis Renewable Energy?

AspectEntry Level Data Science Renewable EnergyEntry Level Data Analysis Renewable Energy
Required CredentialsBachelor's in Data Science, Computer Science, or related field; some roles may prefer certifications in data analysis or programmingBachelor's in Statistics, Mathematics, or related field; certifications in data analysis tools are common
Work EnvironmentTech-focused teams, renewable energy companies, research labsEnergy companies, consulting firms, research organizations
Employer & Industry UsageUtilized for developing predictive models, optimizing energy production, and analyzing large datasetsUsed for interpreting data trends, reporting, and supporting decision-making in renewable projects

While both roles involve working with data in the renewable energy sector, Entry Level Data Science Renewable Energy focuses on building models and advanced analytics, whereas Entry Level Data Analysis Renewable Energy emphasizes interpreting data and generating reports. The credentials overlap but differ slightly in technical depth and tools used.

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Entry Level Data Science Renewable Energy jobs near you
Infographic showing various Entry Level Data Science Renewable Energy job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $42,098 per year, or $20.2 per hour.

Jr. Data Scientist 00021

West Coast Consulting LLC

Westbrook, ME • On-site

Other

Medical, Life

Re-posted 10 days ago

Job description

Job Description
Hybrid -Westbrook, ME
Job Description:
The Machine Intelligence team in R&D is looking for an entry-level Data Scientist to develop machine learning solutions for the hematology analyzers. In this role, you will work on classification and clustering problems on tabular data, with solutions deployed on edge hardware in our analyzer platforms. You will work under the supervision of a senior data scientist who will guide your technical development and project execution. We are looking for a curious, adaptable team player eager to build foundational skills in applied machine learning.
What you can expect:
Develop classification and clustering models on tabular data to support hematology analyzer capabilities
Contribute to model development, evaluation, and iteration under the guidance of a senior data scientist
Partner with senior team members to understand requirements, explore data, and validate model performance
Document your work clearly so it can be reviewed, reproduced, and built upon by the team
Deploy your solutions to edge hardware
What you need to succeed:
0-2 years of experience applying machine learning to real-world problems (internships, research, and coursework projects count)
Strong working knowledge of Python and common data science libraries (pandas, scikit-learn, NumPy)
Solid foundation in statistics, machine learning, and algorithms
Demonstrated understanding of classification and clustering methods for tabular data, including when to apply which approach and how to evaluate results
Curiosity about the data and the underlying generating processes - a habit of asking "why" before reaching for a model
A growth mindset and willingness to learn from more senior team members
Ability to communicate analyses and results clearly to your immediate team
Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus
Nice to have:
Exposure to deploying ML models on resource-constrained or edge hardware
Familiarity with model optimization techniques (quantization, ONNX, TFLite)
Experience with version control (Git) and collaborative software development practices
Experience modeling data for medical, diagnostic or life sciences applications

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