As a Machine Learning and Data Science Engineer Intern, you will apply data science and machine learning methods to operational telemetry from data center facilities and engineering infrastructure.

Working with experienced engineers, you will explore real-time and historical data, develop and evaluate predictive analyses, and communicate findings that can support reliable operations and informed engineering decisions. The internship combines practical analysis, prototype development, and exposure to real-world infrastructure data.

Work with Facilities Reliability Engineering team members to define operational questions that can be addressed through data analysis and machine learning.

Collect, prepare, clean, and explore telemetry datasets under the guidance of experienced team members.

Analyze real-time and historical telemetry to identify patterns, trends, correlations, anomalies, and data-quality issues.

Develop and evaluate predictive models that may help anticipate operational events, trends, or conditions.

Apply appropriate statistical, data science, and machine learning methods to clearly defined operational problems.

Use Python and relevant libraries to create reproducible analyses, experiments, visualizations, and prototype solutions.

Evaluate model performance using appropriate metrics and document the assumptions, limitations, and uncertainty of each approach.

Support repeatable data-processing and analytics workflows that other team members can understand and reproduce.

Collaborate with engineers to validate analytical findings against operational knowledge and incorporate feedback into subsequent work.

Present progress, findings, and recommendations clearly and seek guidance when encountering unfamiliar or complex problems.

Machine Learning and Data Science Engineering Intern

Responsibilities

Qualification

Required

Preferred

Company

Graphcore Early Careers

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