Originally posted 27 October 2026 by the employer.
About the role
As a Data Science Intern at Micron Technology, you will work at the intersection of statistical analysis, machine learning, and AI agents to solve semiconductor manufacturing challenges. You will apply time-series analytics, change-point detection, machine learning, and large language model (LLM) technologies to manufacturing data.
What you'll do
- Apply statistical and machine learning techniques, including time-series analysis, change-point detection, clustering, and dimensionality reduction, to identify trends and anomalies in semiconductor manufacturing data. - Develop and evaluate AI agents and LLM-enabled workflows by integrating analysis tools, designing prompts, and assessing model performance against known outcomes. - Extract, clean, integrate, and analyze data using SQL, Python, and analytical tools to support engineering investigations and decision-making. - Build reproducible analytics, visualizations, dashboards, and reporting solutions that provide actionable insights to engineering teams. - Present technical findings, recommendations, and limitations clearly to engineering partners while supporting continuous improvement initiatives.
What you'll need
- Currently pursuing a Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Electrical Engineering, Computer Engineering, or a related quantitative field, with an expected graduation date between December 2027 and June 2028. - 0-2 years of relevant experience through coursework, research, internships, academic projects, hackathons, or data science competitions. - Experience with Python for data analysis, including libraries such as Pandas, NumPy, or similar tools, and basic SQL for data extraction and preparation. - Knowledge of statistics, machine learning fundamentals, hypothesis testing, regression, clustering, PCA, and data analysis methodologies. - Experience using AI technologies, large language models (LLMs), AI agents, or generative AI platforms through academic, personal, or project-based work.
Nice to have
- Experience with time-series analysis, forecasting, anomaly detection, change-point detection, or statistical modeling techniques. - Familiarity with LLM agents, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), LangGraph, prompt engineering, or AI workflow orchestration frameworks. - Experience evaluating AI model performance, validating outputs, mitigating hallucinations, or developing AI-assisted applications and workflows. - Proficiency with data science and machine learning tools such as Scikit-learn, XGBoost, SHAP, SciPy, R, JMP, Plotly, or similar platforms. - Experience applying analytics, machine learning, or AI technologies to manufacturing, engineering, scientific research, or other real-world datasets.
Skills: Python, SQL, machine learning, time-series, semiconductor manufacturing data
This role has been open 0 days — well below the 60-day median for Yield Engineering roles.
Yield Engineering · Yield Engineering
|
Open roles in category
223
|
Median days open
60 d
|
Median salary
$139k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Micron Technology | All employers we track in this specialty (223 roles · 37 employers) |
|---|---|---|
| Open roles in this specialty | 119 | 223 |
| Median days open | 63 d | 60 d (+3 d vs this employer) |
| Median salary (USD postings) | — | $139k |
Skills observed across this category: Python, SQL, machine learning, time-series, semiconductor manufacturing data
Who's hiring in this category
- Micron Technology (this employer) · 114 open roles · median 63 d
- Intel Corporation · 13 open roles · median 24 d
- GlobalFoundries · 11 open roles · median 175 d
- Skyworks · 8 open roles · median 61 d
- STMicroelectronics · 8 open roles · median 34 d
- Qualcomm · 5 open roles · median 108 d
How we counted: 223 open Yield Engineering (Yield Engineering) roles from 37 employers tracked in the SemiconductorJobs index, counted 27 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there. Figures refresh nightly.