Data Science Co-Op/Intern (Spring/Summer 2027)

• 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 a basic understanding of how these protocols communicate process and sensor data.

• Assists in developing and implementing statistical and machine learning models to solve operational challenges such as predictive maintenance, anomaly detection, and process optimization.
• Uses AI-assisted development tools (for example, Cursor or similar coding assistants) and large language models to accelerate coding, data exploration, documentation, and reporting.
• Supports the application of AI tools and techniques, including generative AI and LLMs, to speed up analysis, automate reporting, and surface insights from complex process datasets, while validating model output before it is used in analysis or recommendations.
• Collaborates with engineering and production teams to deploy data-driven solutions that improve efficiency and quality.

• 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.
• Uses AI-assisted writing and coding tools to draft reports, queries, and visualizations, then reviews and corrects the output before sharing it.

• 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-assisted development, large language models, and industrial data systems.
• Other duties as assigned.

• Actively enrolled in a bachelor's degree (sophomore standing or above) in data science, statistics, computer science, engineering, or a related field.
• Prior exposure to data science, data engineering, or a related field through coursework, personal projects, or other relevant experience.
• Actively enrolled in a bachelor'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/HTTPS, and general connectivity troubleshooting.
• Basic familiarity with relational databases such as PostgreSQL or MySQL, including writing simple queries (SELECT, JOIN, WHERE).
• Basic understanding of AI-assisted development environments (for example, Cursor or similar coding assistants) and of large language models, including how these tools are used to write, review, and debug code and to speed up data analysis.
• Awareness of AI concepts and how they can be applied to data science and analytics workflows, including the need to check model output for accuracy.

• Actively enrolled in a master's degree in data science, statistics, computer science, engineering, or a related field.
• Previous internship or relevant work experience.
• Hands-on experience using an AI-assisted IDE or coding assistant (for example, Cursor) on coursework, personal projects, or prior work.
• 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 or time-series database platforms.

• 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.
• Familiarity with AI-assisted IDEs and coding assistants (for example, Cursor) and the ability to use LLM-based tools effectively and responsibly in data science and analytics work.
• Ability to prompt, review, and correct AI-generated code, SQL, and analysis so that results are accurate before they are shared or acted on.
• 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 queries.
• Understanding of industrial data protocols such as MQTT and OPC-UA and how they are used to transmit process and sensor data.
• Ability to independently troubleshoot basic data access issues, including network connectivity, database permissions, and protocol configuration.
• Analytical mindset with strong problem-solving skills.
• Excellent communication skills, with the ability to explain technical findings to non-technical stakeholders.
• Detail-oriented and proactive, with a desire to learn and grow in the field of data science.

Physical Demands: This position requires sitting at a desk and viewing a computer screen for extended periods while working or performing other office tasks. Ability to move around the office, including occasional standing, walking, and bending to retrieve documents, attend meetings, or use office equipment. Some light lifting may be required, up to 20 lbs.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.