Data Scientist

Duties: Deliver systems solutions for high-volume semiconductor wafer manufacturing environment through data analytics, utilizing machine learning and artificial intelligence applications. Responsible for integration of manufacturing quality, engineering, production, and support organizations on projects to optimize Time to Detect issues, quality solutions and incident prevention. Utilize data analytic techniques, advanced statistical methods, programming skills and semiconductor device manufacturing to extract, load, and model large amounts of data across multiple platforms and sources. Collaborate subject matter experts to determine the practical significance. Participate in factory standardization activities and improvements across global manufacturing footprint. Develop and contribute logical and objective data analysis skills and support data analytics teams across Front end, Assembly, Electronic Business Intelligence and IT with a customer protection perspective and, problem solving and prevention in semiconductor manufacturing environments.

Master's degree in Computer Science, Data Science, foreign equivalent or related field.

Position requires knowledge gained via completion of a graduate-level course, research project, internship or related occupation involving:

1. Programming language in C++, Python, and SQL

2. Pandas and OpenCV,

3. Computer compiler and algorithms,

4. Machine learning,

5. Deep learning language

6. Network infrastructure

7. Neural networks such as CNN,

8. Data visualization software and

9. PyTorch and Sklearn.

Salary: $128750 per year.

NXP is an Equal Opportunity/Affirmative Action Employer regardless of age, color, national origin, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, marital status, status as a disabled veteran and/or veteran of the Vietnam Era or any other characteristic protected by federal, state or local law. In addition, NXP will provide reasonable accommodations for otherwise qualified disabled individuals.