À propos
In the Global Products Group, we are dedicated to excellence in the design and engineering of Lams etch and deposition products. We drive innovation to ensure our cutting-edge solutions are helping to solve the biggest challenges in the semiconductor industry. The impact youll make
Join Lam as a Data Scientist, where you'll design, develop, and program methods to analyze unstructured and diverse big data into actionable insights. You'll develop algorithms and automated processes to evaluate large data sets from disparate sources. Your expertise in generating, interpreting, and communicating actionable insights enables Lam to make informed and data?driven decisions. What Youll Do
Develop machine learning models, statistical algorithms, and data?driven frameworks to analyze engineering, sensor, metrology, and equipment telemetry data. Conduct advanced image processing and computer vision analysis on wafer, chamber, or process images to derive meaningful features and insights. Apply Bayesian optimization to accelerate engineering workflows, experimental design, and process parameter tuning. Build scalable data pipelines to integrate structured and unstructured data sources. Perform exploratory data analysis, feature engineering, uncertainty quantification, and model validation. Partner with engineers and domain experts to translate physics? or chemistry?based phenomena into analytic or computational models. Deploy and maintain production?level analytical solutions and monitor their long?term performance. Visualize results effectively and communicate findings to both technical and non?technical stakeholders. Use sound statistical design principles (DOE, hypothesis testing, modeling assumptions) to guide experimentation and decision?making. Contribute to best practices in version control, documentation, reproducibility, and model governance. Who Were Looking For
Ph.D. in Statistics, Computational Physics, Computer Science, or Applied Mathematics. Preferred Qualifications
Required Skills & Experience:
Strong proficiency with Python (NumPy, SciPy, Pandas, scikit?learn, etc.). Demonstrated experience with advanced image processing (e.g., OpenCV, scikit?image, deep learningbased methods). Solid foundation in machine learning, statistical inference, and scientific computing. Experience working with large, complex data sets. Strong analytical and problem?solving skills, particularly in complex technical domains. Ability to collaborate in cross?functional teams and communicate complex concepts clearly. Experience with physics?based machine learning and Bayesian statistics is a plus. Our commitment
We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results. Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non?discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees. Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on?site collaboration with colleagues and the flexibility to work remotely and fall into two categories On?site Flex and Virtual Flex. On-site Flex youll work 3+ days per week on?site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. Virtual Flex youll work 1?2 days per week on?site at a Lam or customer/supplier location, and remotely the rest of the time. #J-18808-Ljbffr
Compétences linguistiques
- English
Avis aux utilisateurs
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