Senior Data Scientist

Samsung Electronics•Taylor, TX

1d•$90,000 - $174,500•Onsite

About The Position

As a Senior Data Scientist at Samsung Austin Semiconductor, you will build and deploy machine learning systems that directly improve our semiconductor manufacturing process. Your work will center on anomaly detection, root cause analysis, and virtual metrology. You will spend most of your time working with high-frequency time-series and tabular data, engineering features, training models, and ensuring your results are clear and actionable for process engineers. You will own the full model lifecycle: data preparation, algorithm selection, deployment, monitoring, and ongoing tuning. The team operates in a collaborative, sprint-driven environment where you will have the autonomy to design your own technical approaches, test new methods, and iterate quickly based on feedback. Prior semiconductor experience is helpful but not required; you will learn the domain through hands-on projects and direct support from the team.

Requirements

Bachelor’s degree or higher in Data Science, Statistics, Computer Science, Physics, or a related quantitative field (Master’s or PhD preferred).
5+ years of professional experience designing, training, and deploying machine learning models.
Solid working knowledge of regression, classification, ensemble methods, feature engineering, and model evaluation metrics.
Advanced proficiency in Python for data analysis and modeling, plus strong SQL skills for extracting and transforming large datasets.
Experience building and maintaining ML pipelines for experiment tracking, model versioning, automated retraining, and live performance monitoring.

Nice To Haves

A track record of turning open-ended questions into clear machine learning problems and delivering models that run reliably in production.
Hands-on experience with tabular data techniques like categorical encoding, missing value imputation, and model interpretability methods such as SHAP.
Comfort working in an Agile environment where you can prototype quickly, validate results with real data, and refine models based on direct feedback from engineering teams.
Practical experience using PySpark to process, transform, and scale large datasets for machine learning workflows.

Responsibilities

Build supervised machine learning 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 that ensure model accuracy, relevance, and integrity.
Design, train, and iterate on predictive models that integrate various process and quality data sources emphasizing algorithm selection, feature engineering, and rigorous model validation.
Engineer features from continuous time-series streams, combine process variables meaningfully, and build reliable methods for handling missing or sparse data.
Manage the full modeling workflow including cross-validation, hyperparameter tuning, experiment tracking, model versioning, and automated retraining schedules.
Set clear statistical benchmarks for model performance and monitor deployed models in production.
Communicate complex technical findings to both fellow data scientists and process engineers.