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Internship Applied Scientist Machine Learning Jobs in Spring, TX

Dow

Machine Learning Engineer

Houston, TX · On-site

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

bp

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Applied knowledge of data science and ML tools across all stages of the data and model lifecycle. * Thorough understanding of the mathematical foundations of statistics, machine learning, and ...

Weatherford

Data Scientist - Wireline

Houston, TX

... machine learning, and scientific modeling to solve complex problems related to downhole tools, signal processing, and formation evaluation. The ideal candidate combines strong fundamentals in applied ...

Beth Page tech

Data Scientist

Houston, TX · On-site

Data Science & Machine Learning: * Strong foundation in mathematics, statistics, and machine learning * Experience with exploring and extracting insights from multi-dimensional datasets * Proficiency ...

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Corva

Senior Data Scientist

Houston, TX · On-site

Design, develop, and deploy advanced machine learning and deep learning models for anomaly ... Conduct applied research in drilling, completions, and geoscience domains * Design rigorous ...

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All JobsApplied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning Jobs in Spring, TX

Internship Applied Scientist Machine Learning information

See Spring, TX salary details

$22.7K

$37.9K

$78.3K

How much do internship applied scientist machine learning jobs pay per year?

As of Aug 6, 2026, the average yearly pay for internship applied scientist machine learning in Spring, TX is $37,895.00, according to ZipRecruiter salary data. Most workers in this role earn between $28,900.00 and $40,900.00 per year, depending on experience, location, and employer.

What types of projects do internship applied scientists in machine learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

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

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What does an internship applied scientist in machine learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.
What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Spring, TX? For Internship Applied Scientist Machine Learning jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Internship Applied Scientist Machine Learning jobs in Spring, TX look for? The top searched job categories for Internship Applied Scientist Machine Learning jobs in Spring, TX are:
What cities near Spring, TX are hiring for Internship Applied Scientist Machine Learning jobs? Cities near Spring, TX with the most Internship Applied Scientist Machine Learning job openings:
Internship Applied Scientist Machine Learning jobs near you
Infographic showing various Internship Applied Scientist Machine Learning job openings in Spring, TX as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 23% Part Time, 1% Temporary, and 4% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $37,895 per year, or $18.2 per hour.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International LLC

Houston, TX

Full-time

Posted 16 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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