Senior Data Scientist

Samsung Electronics Co., Ltd.$90K — $174K *
Manufacturing & Automotive
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree or higher in a quantitative field; Master's or PhD preferred.
  • 5+ years of experience in designing, training, and deploying machine learning models.
  • Strong knowledge of regression, classification, ensemble methods, feature engineering, and model evaluation metrics.
  • Advanced Python skills for data analysis and modeling; strong SQL skills.
  • Experience with ML pipelines for tracking, versioning, and monitoring models.

Responsibilities

  • Build supervised machine learning models for anomaly detection using time-series and tabular data.
  • Enhance data workflows to ensure model accuracy and integrity.
  • Design, train, and iterate on predictive models with diverse data sources.
  • Engineer features from time-series streams and handle data sparsity.
  • Manage the full modeling workflow including validation and retraining schedules.
  • Set performance benchmarks and monitor models in production.
  • Communicate findings effectively with data scientists and engineers.

Benefits

  • 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.
  • Generous paid time off (PTO) including personal holidays and regular holidays.
  • Wellness incentives and more.
Full Job Description
Position Summary

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.

Role and Responsibilities

Here's What You'll Be Responsible For:
  • 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.


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.

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