Data Scientist

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As a Data Scientist at Micron Technologies, you will play a crucial role in analyzing and extracting valuable insights from large datasets related to semiconductor manufacturing, process optimization, and quality control. You'll collaborate with multi-functional teams to develop innovative solutions that improve product performance, yield, and reliability.

RESPONSIBILITIES

• Data Analysis: Perform exploratory data analysis (EDA) on semiconductor manufacturing data, including wafer fabrication, yield, and defect rates.

• Model Development: Develop predictive models using machine learning techniques (such as regression, classification, and clustering) to optimize manufacturing processes and improve product quality.

• Feature Engineering: Extract relevant features from raw data, considering factors like material properties, process parameters, and environmental conditions.

• Anomaly Detection: Identify anomalies and outliers in semiconductor production data, suggesting corrective actions to improve yield.

• Collaboration: Work closely with engineers, physicists, and domain experts to understand semiconductor processes and translate business requirements into data-driven solutions.

• Visualization: Build clear and informative visualizations to communicate findings and insights to collaborators.

• Continuous Improvement: Stay up to date with industry trends, research advancements, and emerging technologies in data science and semiconductor manufacturing.

QUALIFICATIONS

• Master’s or Ph.D. in Data Analytics, Data Science, or related field with practical experience with Virtual Metrology, AI, process control, yield improvement, quality control, semiconductor process engineering, image analytics, or related semiconductor field focus.

• 1 - 3 years prior experience in semiconductor manufacturing, yield optimization, or process control.

Technical Skills:

• Proficiency in Python,

• Strong knowledge of machine learning algorithms and statistical techniques.

• Familiarity with web application and reporting, Experience with SQL and database management

Technologies:

• ML frameworks:  Decision trees, Clustering, Regression, Neural Networks, NLP, Pytorch, Big Data, Tensorflow, Pandas, Streamlit

Tools and technologies:

• Docker, Containerization, Kubernetes, Jenkins, Ansible, Elasticsearch, SSL, Microservices, data visualization tools (e.g., Streamlit, Tableau, PowerBI, etc.).

Cloud expertise:

• Google Cloud Platform, OpenShift Container Platform

AI Skills:

AI specialization and Virtual Metrology/Modeling/Computation: 

• Python library sklearn, xgboost, pycaret, etc. with the ability to learn any new package.

• Knowledgeable with the general process of data science work like data extraction, feature extraction, feature selection and modeling.

• Familiar with the skills to take care of time series data (feature extraction, modelling, similarity measure of time series).

Image specialization:

• Familiar with deep learning network CNN and package keras.

Frontend:

• Languages: HTML, CSS, JavaScript, Typescript

• Frameworks/Libraries: Angular, HighCharts, Plotly, AG Grid, BootStrap, Ngx Bootstrap, RxJS

• Packaging: npm, Jenkins

Backend/API:

• Languages: Python,

• Preferred, but not required: Java, C#, PHP, Perl, C++

• Frameworks: Flask, FastAPI, Experience with RESTful APIs, Minor experience with WSLs

• Databases: MySQL, MSSQL, Oracle, PostgreSQL, MongoDB, Neo4j, BigQuery SQL, Snowflake SQL, basic understanding of SSL

Tools:

• Git, Bitbucket

Soft Skills:

• Analytical approach and problem-solving abilities.

• Strong communication skills to work effectively with teams from various functions.

• Adaptability and willingness to learn new technologies and methodologies.

Project Skills:

• AGILE, Waterfall, Scrum, Software Development Lifecycle, JIRA, Change management, Release management, Configuration management