Applied Materials, Inc. is the global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations make possible the technology shaping the future.
Are you inspired by how data analytics can be used to diagnose, improve and add value to hardware? Are you a natural team player who loves to solve complex problems? If yes, then you’ll fit right in here at Applied Materials.
We are a fast-growing team of doers who are bringing domain knowledge-aided data analytics to our semiconductor equipment. We are working passionately to transform our customers’ experiences in ground-breaking ways new to this industry. We will give you the guidance, tools and support you need on this journey with us that is rewarding, fulfilling and fun!
We are seeking talented individuals with general expertise and demonstrated interest in hardware analytics, systems engineering and data science. You will be a member of the Analytics Team that is responsible for ensuring systems engineering driven data analytics solutions to our customers. Your work will involve developing tools to collect, analyze, and visualize diverse data sets to address a variety of high value engineering issues.
Key activities include:
- Design, build and maintain big data workflows/pipelines to process terabytes of data
- Fine tune application performance, troubleshoot and resolve data processing issues
- Develop tools and algorithms to collect, analyze and visualize data
- Provide end-to-end solution for a given problem and effectively communicate solutions to the team
The ideal candidate should be comfortable working cross-functionally as well as delivering results independently. The position requires willingness to learn new technologies, solving complex problems, identifying innovative solutions and troubleshooting. Requirements include:
- Earning a degree in a quantitative field (e.g. Computer Science, Engineering, Statistics, Chemical Engineering, Mechanical Engineering, Electrical Engineering)
- Understanding of physical systems (e.g. signals processing, sensors, semiconductors, manufacturing, microfluidics)
- Experience with machine learning or other statistical data analysis techniques, such as regression, time series analysis, hypothesis testing, classification, or clustering
- Experience performing data extraction, cleaning, analysis, and visualization for medium to large datasets
- Experience with at least one programming language (Python, R, Java, etc.) and writing SQL queries
- Experience with scientific computing packages such as scikit-learn, numpy, scipy, pandas, dplyr, or ggplot2
Applied Materials is committed to diversity in its workforce including Equal Employment Opportunity for Minorities, Females, Protected Veterans and Individuals with Disabilities.