Data Scientist 2

Lam Research•Fremont, CA

7h•$86,000 - $183,000•Hybrid

About The Position

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 will develop algorithms and automated processes to evaluate large data sets from disparate sources, enabling informed, data‑driven decisions across the organization. In this role, you will leverage your computer science expertise and experience working with large-scale data to solve real-world scientific and engineering challenges in cutting-edge semiconductor manufacturing and research. Your work will directly impact how complex problems are understood, diagnosed, and solved in highly advanced processing environments. This role requires a strong curiosity for how physical systems behave and a motivation to apply data science methods to uncover underlying mechanisms. You will work at the intersection of data, science, and engineering, helping translate complex data into meaningful insights that drive both immediate problem-solving and long-term innovation.

Requirements

Bachelor’s degree + 2years experience or Master's degree in Data Science, Statistics, Computer Science, Engineering, Applied Mathematics, or a related quantitative field.
Strong foundation in data analysis, statistical modeling, and machine learning, with the ability to apply these methods to real‑world engineering problems.
Hands‑on experience programming in Python (required), including common data science libraries (e.g., NumPy, pandas, SciPy, scikit‑learn, PyTorch, TensorFlow, or similar).
Experience working with large, noisy, and heterogeneous datasets, including data cleaning, feature extraction, and validation.
Ability to translate ambiguous problem statements into well‑defined analytical approaches and actionable insights.
Strong communication skills, with the ability to explain technical results clearly to audiences with varied levels of technical expertise.

Nice To Haves

Experience applying data science techniques in complex industrial environments.
Familiarity with equipment health monitoring, fault detection and classification (FDC), predictive maintenance, or time‑series analysis.
Experience developing analytics tools or applications with user‑friendly interfaces (e.g., dashboards, internal web apps, or visualization tools) for non‑programmers.
Knowledge of SQL, data pipelines, or working with structured and unstructured production data sources.
Experience with cloud‑based or distributed computing environments is a plus.
Understanding of experimental design, root‑cause analysis, or process characterization in a hardware‑driven environment.
Ability to balance quick‑turn analysis for escalations with longer‑term, robust algorithm development

Responsibilities

Partner closely with subject matter experts to understand the problem, identify available data, perform quantitative analysis, interpret results, and clearly communicate insights to the broader engineering team when addressing a customer escalation.
Develop analytics applications with intuitive, user‑friendly interfaces that enable engineers—without programming expertise—to diagnose and troubleshoot similar issues efficiently in the future.
Design and implement algorithms that identify trends and signals in tool and process data to help predict potential failures before they occur, improving uptime and customer confidence.