Data Science Internship: Predictive Analytics for Energy AI

ON.energy; Ciudad de México; Presencial; MXN 134.000 - 201.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive salary + annual bonus

Major medical expenses and life insure

Paid time off and holidays

Professional development opportunities

Descripción de la vacante

ON.energy is building the backbone of energy and AI infrastructure powering data centers and mission-critical facilities. As a Data Science Intern, you will support the team across the full data workflow—from understanding the problem to preparing data, running analyses, and assisting with models.

You will learn and apply best practices in Data Science and Machine Learning, perform data ingestion, cleaning, exploration, and contribute to predictive maintenance and anomaly-detection projects.

Formación

• Solid foundation in quantitative methods gained through coursework.
• Strong analytical mindset and problem-solving ability.
• Ability to structure and analyze data across projects.

Responsabilidades

• Understand business problems and data context for each project.
• Analyze and document available data sources and workflows.
• Ingest, clean, and structure data for analysis.
• Perform exploratory data analysis and build visualizations.
• Assist in modeling, experiments, and evaluation of models.
• Document processes and results; collaborate on continuous improvement.

Conocimientos

Quantitative analysis

Data science basics

Research experience

Educación

Final semester in Mathematics, Statistics, Physics, Engineering, Data Science, or related field

Descripción del empleo

ON.energy is building the backbone of energy and AI infrastructure powering data centers and mission-critical facilities. As a Data Science Intern, you will support the team across the full data workflow—from understanding the problem to preparing data, running analyses, and assisting with models.

You will learn and apply best practices in Data Science and Machine Learning, perform data ingestion, cleaning, exploration, and contribute to predictive maintenance and anomaly-detection projects.