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.
Skills and Qualifications
Here's what you'll need
Required
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.
Preferred
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.
The current base salary range for this role is between $90,000 - $174,500. Individual base pay rates will depend on factors including duties, work location, education, skills, qualifications and experience. Total compensation for this position will include a competitive benefits package and may include participation in company incentive compensation programs, which are based on factors to include organizational and individual performance.
Total Rewards
At Samsung Austin Semiconductor, base pay is just one part of our total compensation package. The base compensation for this role will depend on education, experience, skills, and location.
We offer a comprehensive benefits package, including
Medical, dental, and vision insurance
Life insurance and 401(k) matching with immediate vesting
Onsite café(s) and workout facilities
Paid maternity and paternity leave
Paid time off (PTO) + 2 personal holidays and 10 regular holidays
Wellness incentives and MORE
Eligible full-time employees (salaried or hourly) may also receive MBO bonuses based on company, division, and individual performance.
All positions at Samsung Austin Semiconductor are full-time on-site.
Trade Secrets
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