Data Scientist 4

The group you’ll be a part of

The Office of the CTO is where innovation takes center stage. We inspire our global technical community to take on grand challenges, understand emerging trends, identify the critical inflections, and drive our sustainability, Environment, Social, and Governance (ESG) practices that will define the next generation of semiconductors and continued impact.    

The impact you’ll 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.

Key Responsibilities

• Analyze large, complex datasets from diverse sources to uncover insights and identify opportunities for innovation.
• Design, build, and deploy robust machine learning models with meaningful uncertainty quantification.
• Perform rigorous data engineering and model evaluation, including feature engineering, hyperparameter tuning, and model selection.
• Collaborate with engineering teams to integrate models into production codebases, promoting best practices in code quality and maintainability.
• Communicate findings and technical results clearly to both technical and non-technical stakeholders.

Required Qualifications

• Master’s degree with 6+ years of experience or Ph.D. with 3+ years in Computer Science, Engineering, Physics, Applied Mathematics, Statistics, or a related quantitative field.
• Machine Learning Expertise: Strong theoretical foundation and hands-on experience in ML algorithms, deep learning, AI, statistics, or optimization.
• Programming Skills: Proficient in Python, with motivation to write efficient, maintainable, testable, and well-documented code.
• ML Frameworks: Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Problem Solving: Demonstrated analytical and critical thinking skills, with a track record of delivering impactful R&D solutions.
• Team Collaboration: Proven success working in cross-functional teams with strong execution and communication skills.

Preferred qualifications

• Domain expertise in semiconductor engineering, Bayesian statistics, process engineering, multi-physics modeling, or numerical simulation.
• Familiarity with Linux/Unix operating systems.
• Experience with MLOps tools and principles (e.g., Docker, CI/CD pipelines).

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’ you’ll 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’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.

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