Senior ML Scientist - Anomaly Detection for Manufacturing

Samsung Electronics America; Temple (TX); On-site; USD 90,000 - 175,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

401(k) Matching

Onsite Gym

Job summary

Samsung Austin Semiconductor is seeking a Senior Data Scientist to build and deploy machine learning systems that improve semiconductor manufacturing processes. You will own the full model lifecycle from data prep to deployment, focusing on anomaly detection, root-cause analysis, and virtual metrology.

You will work with high-frequency time-series data and tabular datasets, engineering features, training models, and communicating results clearly to process engineers and fellow data scientists.

Qualifications

• Bachelor’s degree or higher in Data Science, Statistics, CS, Physics or related field.
• 5+ years of experience designing, training, deploying ML models.
• Strong knowledge of regression, classification, ensemble methods, feature engineering, and metrics.
• Proficiency in Python for data analysis and modeling; strong SQL skills.

Responsibilities

• Build supervised ML models for anomaly detection using high-frequency data.
• Enhance data processing workflows to create high-quality test datasets.
• Design, train, and iterate predictive models integrating process and quality data.
• Engineer features from time-series streams and handle missing data.
• Manage complete modeling workflow: cross-validation, tuning, experiment tracking, versioning, retraining.
• Set statistical benchmarks and monitor models in production.
• Communicate findings to data scientists and process engineers.

Skills

Python

SQL

ML pipelines

Model evaluation

Regression/Classification

SHAP interpretability

Time-series analysis

Education

Bachelor’s degree or higher in a quantitative field

Master’s or PhD preferred

Tools

PySpark

Job description

Samsung Austin Semiconductor is seeking a Senior Data Scientist to build and deploy machine learning systems that improve semiconductor manufacturing processes. You will own the full model lifecycle from data prep to deployment, focusing on anomaly detection, root-cause analysis, and virtual metrology.

You will work with high-frequency time-series data and tabular datasets, engineering features, training models, and communicating results clearly to process engineers and fellow data scientists.