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What you’ll work on

We are seeking a data-driven Entry-Level Data Scientist to join our semiconductor engineering team.

As a member of a cross-functional engineering team, you will collaborate with Product, Test, Process, Yield, and Quality Engineers to analyze manufacturing test data, develop predictive and diagnostic models, and implement automated solutions that improve operational process, product quality, and yield performance.

Analyze wafer sort, final test, and inline process data to identify trends, anomalies, and root causes.
Develop dashboards and visualizations to monitor key metrics across product and test stages.

From the employer’s posting

We are seeking a data-driven Entry-Level Data Scientist to join our semiconductor engineering team. This role is ideal for recent graduates who are passionate about applying data science and automation to solve complex engineering challenges and improve the manufacturing process.

As a member of a cross-functional engineering team, you will collaborate with Product, Test, Process, Yield, and Quality Engineers to analyze manufacturing test data, develop predictive and diagnostic models, and implement automated solutions that improve operational process, product quality, and yield performance. You will play a key role in working with suppliers and deploying the quality shield into production line and sustaining.

Key Responsibilities Analyze wafer sort, final test, and inline process data to identify trends, anomalies, and root causes. Develop dashboards and visualizations to monitor key metrics across product and test stages.

Analyze wafer sort, final test, and inline process data to identify trends, anomalies, and root causes. Develop dashboards and visualizations to monitor key metrics across product and test stages. Automate data pipelines and reporting tools to support continuous improvement initiatives.

What you’ll bring

All qualifications

Core experience

Bachelor’s degree in Data Science, Electrical Engineering, Computer Science, Statistics, or related field.
Proficiency in Python or R, with experience using libraries such as pandas, NumPy, scikit-learn, matplotlib.
Familiarity with SQL and working with large-scale databases.
Experience with data visualization tools (e.g., Tableau, Power BI) is an advantage.
Ability to interpret complex datasets and communicate findings clearly to engineering teams.

Preferred experience

Understanding of semiconductor manufacturing and test processes is a plus.

Qualification wording

Bachelor’s degree in Data Science, Electrical Engineering, Computer Science, Statistics, or related field.

Proficiency in Python or R, with experience using libraries such as pandas, NumPy, scikit-learn, matplotlib.

Familiarity with SQL and working with large-scale databases.

Experience with data visualization tools (e.g., Tableau, Power BI) is an advantage.

Ability to interpret complex datasets and communicate findings clearly to engineering teams.

Understanding of semiconductor manufacturing and test processes is a plus.

Tools in this posting

Python
R
SQL
Tableau
NumPy
pandas
Power BI
Matplotlib
scikit-learn

Source

- Strong foundation in statistics, data analysis, and machine learning. - Proficiency in Python or R, with experience using libraries such as pandas, NumPy, scikit-learn, matplotlib. - Familiarity with SQL and working with large-scale databases.

- Proficiency in Python or R, with experience using libraries such as pandas, NumPy, scikit-learn, matplotlib. - Familiarity with SQL and working with large-scale databases. - Understanding of semiconductor manufacturing and test processes is a plus.

- Understanding of semiconductor manufacturing and test processes is a plus. - Experience with data visualization tools (e.g., Tableau, Power BI) is an advantage. - Ability to interpret complex datasets and communicate findings clearly to engineering teams.

Job description

View original posting ↗

About the Role

We are seeking a data-driven Entry-Level Data Scientist to join our semiconductor engineering team. This role is ideal for recent graduates who are passionate about applying data science and automation to solve complex engineering challenges and improve the manufacturing process.
As a member of a cross-functional engineering team, you will collaborate with Product, Test, Process, Yield, and Quality Engineers to analyze manufacturing test data, develop predictive and diagnostic models, and implement automated solutions that improve operational process, product quality, and yield performance. You will play a key role in working with suppliers and deploying the quality shield into production line and sustaining.
This is a unique opportunity to work at the intersection of semiconductor technology, advanced analytics and artificial intelligence.

Key Responsibilities

Analyze wafer sort, final test, and inline process data to identify trends, anomalies, and root causes.
Develop dashboards and visualizations to monitor key metrics across product and test stages.
Automate data pipelines and reporting tools to support continuous improvement initiatives.
Collaborate with cross-functional engineering teams and suppliers to integrate function with production line system, deploy and sustain, and translate data insights into actionable improvements.
Innovate and develop AI/ML solutions for non-standard problems.
Receive training, guidance, and mentorship to accelerate your career growth.

Requirements

Bachelor’s degree in Data Science, Electrical Engineering, Computer Science, Statistics, or related field.
Strong foundation in statistics, data analysis, and machine learning.
Proficiency in Python or R, with experience using libraries such as pandas, NumPy, scikit-learn, matplotlib.
Familiarity with SQL and working with large-scale databases.
Understanding of semiconductor manufacturing and test processes is a plus.
Experience with data visualization tools (e.g., Tableau, Power BI) is an advantage.
Ability to interpret complex datasets and communicate findings clearly to engineering teams.
Strong problem-solving skills, attention to detail, and a collaborative mindset.
Internship or academic project experience in manufacturing, electronics, or data analytics is a plus.

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