Data Science Intern

Empower Your Future: At MetOx International, we’re pioneering the next era of energy security and abundance through breakthrough superconducting technology. As a Data Science Intern, you’ll join a dynamic team committed to strengthening the world’s energy systems to make them more resilient, efficient, and reliable.

 

Based at our global headquarters in Houston, TX, the Data Scientist Intern supports data-driven decision-making across our operations. This intern role focuses on collecting, analyzing, and interpreting complex data sets, including process and operational data, to help improve our production processes, optimize resource allocation, and enhance product performance. This position reports to the Chief Information Officer.

 

Key Responsibilities

• Data Collection & Analysis
  • Assists in gathering, cleaning, and preprocessing data from multiple sources including relational databases, process historians, sensors, and operational systems to ensure data accuracy and completeness.
  • Assists in conducting exploratory data analysis (EDA) to identify patterns, trends, and relationships within production and operational data.
  • Assists in troubleshooting data retrieval issues, including network connectivity errors, permission issues, and data access failures from industrial and enterprise data sources.

• Supports data ingestion from industrial protocols such as MQTT and OPC-UA, including basic understanding of how these protocols communicate process and sensor data.

• Modeling, Machine Learning & AI
  • Assists in developing and implementing statistical and machine learning models to solve operational challenges such as predictive maintenance, anomaly detection, and process optimization.
  • Supports the application of AI tools and techniques including generative AI and large language models (LLMs) to accelerate data analysis, automate reporting, and surface insights from complex process datasets.
  • Collaborates with engineering and production teams to deploy data-driven solutions that improve efficiency and quality.
• Reporting & Visualization
  • Creates data visualizations and dashboards to communicate insights and findings to non-technical stakeholders.
  • Prepares reports summarizing data analysis results, with clear recommendations based on data insights.
• Collaboration & Continuous Learning
  • Works with cross-functional teams to understand business needs and translate them into data science objectives.
  • Stays current with industry trends, tools, and best practices in data science, AI, and industrial data systems to enhance personal and team capabilities.
• Other duties as assigned.

 

Minimum Qualifications:  

• Prior exposure to data science, data engineering, or a related field through coursework, personal projects.
• Actively enrolled in abachelor’s degree in data science, statistics, computer science,
• engineering, or a related field at an accredited university.
• Basic understanding of networking concepts, including TCP/IP, DNS, HTTP/S, and general connectivity troubleshooting.
• Basic familiarity with relational databases such as PostgreSQL or MySQL, including writing simple queries (SELECT, JOIN, WHERE).
• Awareness of AI concepts and how they can be applied to data science and analytics workflows.

 

Preferred Qualifications: 

• Actively enrolled in a master’s degree in data science, statistics, computer science, engineering, or a related field.
• Previousinternship or relevant work experience. 
• Experience with industrial communication protocols such as MQTT or OPC-UA.
• Familiarity with time-series data analysis and process historian platforms (e.g., OSIsoft PI, InfluxDB).
• Exposure to SCADA systems or industrial IoT environments.
• Hands-on experience with PostgreSQL, MySQL, or other relational/time-series database platforms.

 

Knowledge, Skills, & Abilities: 

• Proficiency or strong working knowledge of programming languages commonly used in data science (Python or R).
• Familiarity with data visualization concepts and data processing libraries (Pandas, NumPy).
• Basic understanding of machine learning techniques and model evaluation methods.
• Awareness of AI and generative AI tools and how they can be applied to data science and operational analytics.
• Basic knowledge of relational databases (PostgreSQL, MySQL) and the ability to write and interpret SQL q