Senior Data Scientist — Real-Time ML for Semiconductor Ops

Samsung Electronics; Wisconsin; On-site; USD 90,000 - 175,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Life insurance and 401(k) matching

Job summary

Samsung Austin Semiconductor is seeking a Senior Data Scientist to build and deploy ML systems that improve our semiconductor manufacturing process. You will focus on anomaly detection, root cause analysis, and virtual metrology, working with high-frequency time-series and tabular data.

You will own the full model lifecycle from data prep to deployment and monitoring. You will join a collaborative, sprint-driven team that encourages you to design and test new methods, iterate quickly, and learn

Qualifications

• 5+ years of professional experience designing, training, and deploying ML models.
• Strong knowledge of regression, classification, ensemble methods, and evaluation metrics.
• Advanced proficiency in Python for data analysis and modeling, plus strong SQL skills.

Responsibilities

• Build supervised ML models for anomaly detection using high-frequency tabular and time-series data.
• Enhance data collection and processing workflows to create robust, high-quality test datasets.
• Design, train, and iterate predictive models that integrate various process and quality data sources.
• Engineer features from continuous time-series streams and handle missing data.
• Manage the full modeling workflow including cross-validation, hyperparameter tuning, experiment tracking, versioning, and retraining schedules.
• Set clear statistical benchmarks and monitor deployed models in production.
• Communicate complex technical findings to both data scientists and process engineers.

Skills

Python

SQL

ML pipelines

Feature engineering

Model evaluation

Time-series

Data science

Education

MS/PhD preferred

Tools

PySpark

Job description

Samsung Austin Semiconductor is seeking a Senior Data Scientist to build and deploy ML systems that improve our semiconductor manufacturing process. You will focus on anomaly detection, root cause analysis, and virtual metrology, working with high-frequency time-series and tabular data.

You will own the full model lifecycle from data prep to deployment and monitoring. You will join a collaborative, sprint-driven team that encourages you to design and test new methods, iterate quickly, and learn