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Internship Graduate Machine Learning Jobs in Houston, TX

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Internship Graduate Machine Learning information

See Houston, TX salary details

$24.4K

$40.7K

$84K

How much do internship graduate machine learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for internship graduate machine learning in Houston, TX is $40,666.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,000.00 and $43,900.00 per year, depending on experience, location, and employer.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What are the key skills and qualifications needed to thrive as an internship graduate machine learning, and why are they important?

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What is an internship graduate machine learning?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What types of projects do internship graduate machine learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.
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Internship Graduate Machine Learning jobs near you
Infographic showing various Internship Graduate Machine Learning job openings in Houston, TX as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $40,666 per year, or $19.6 per hour.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International LLC

Houston, TX

Full-time

Posted 17 days ago

Job description

Application Deadline: September 1, 11:59pm EST

Program Summary - Commercial Technology Internships

Company Overview:

Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.

Position Overview:

CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.

Responsibilities:

  • Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
  • Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
  • Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
  • Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
  • Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
  • Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

Qualifications:

  • Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
  • Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
  • Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
  • Strong analytical skills with demonstrated attention to detail.

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