Data Scientist 4

Lam Research

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Tualatin, OR
Posted: 10 days ago
Freshness: Confirmed live yesterday
Closes: Mar 29, 2027
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $181k

$124k $236k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $151,493–$211,200.

Based on 240 similar postings.

Employer

About Lam Research

Lam Research Corporation is a leading American supplier of wafer-fabrication equipment and services to the global semiconductor industry.

Lam Research currently has 138 open roles on FindRole.

Listed pay typically runs $119,000–$261,000 across 85 roles with salary data.

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At a glance

TL;DR · Data Scientist 4

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The Data Scientist 4 joins the Office of the CTO within the Semiverse Solutions team to develop software products and virtual twins for semiconductor fabrication. This senior role involves owning the end-to-end development of surrogate models, virtual twins, and optimization workflows. You will build and scale physics-aware machine learning models, design numerical experiments to generate synthetic training data, and implement optimization algorithms like Bayesian optimization and active learning. The work requires applying statistical analysis and machine learning methods to multi-physics plasma modeling and 3D feature scale modeling. You will write production-quality Python code to solve complex engineering challenges. Key requirements include expertise in physics-aware ML architectures, numerical simulation data, and semiconductor process physics, while collaborating with computational physicists and software developers to create practical solutions.

What does a Data Scientist earn?

Median $173200 from 351 postings across 62 companies.

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Skills

Python Machine Learning Data Science Bayesian Optimization Active Learning Statistical Analysis Numerical Simulation HPC SLURM Scientific Computing Physics-aware ML Distributed Compute

What you'll do

Build and scale physics-aware machine learning models and surrogate models to accelerate complex multi-physics simulations.
Apply statistical analysis and machine learning methods to both simulated and experimental datasets.
Implement optimization algorithms like Bayesian optimization and active learning to accelerate process design.
Run simulations to generate training data and design effective sampling strategies for model development.
Write robust, production-quality Python code following modern software engineering practices.
Develop surrogate models, virtual twins, and optimization workflows from end-to-end design to deployment.

What we're looking for

Master’s degree with 6+ years of relevant experience or a Ph.D. with 3+ years of relevant experience.
Degree in Physics, Engineering, Applied Mathematics, Computer Science, or a related quantitative field.
Demonstrated ability to build, train, evaluate, and tune machine learning models end-to-end on real-world datasets.
Solid foundations in statistics and core machine learning algorithms.
Experience building surrogate models, physics-aware ML architectures, or optimization algorithms like Bayesian optimization.
Strong understanding of physical sciences such as transport phenomena, kinetics, or semiconductor process/device physics.
Strong Python programming skills to write modular, testable, and maintainable code.
Professional or academic experience in the semiconductor industry, processing, or device physics (preferred).

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