Data Scientist III - (B3)

Applied Materials • Santa Clara, CA

3d

About The Position

Optimize operational workflows by designing and deploying ML models that improve efficiency, reduce manual effort, and support data‑driven decision-making across business units. Develop autonomous, agentic AI systems that can proactively monitor processes, make recommendations, trigger actions, and continuously improve through feedback loops. Interpret and analyze complex datasets using advanced statistical and machine learning techniques; communicate insights clearly to technical and non‑technical stakeholders. Build scalable data collection and analytics pipelines that enhance data quality, statistical efficiency, and real-time decisioning. Develop and validate custom ML models (supervised, unsupervised, time-series, causal inference, etc.) tailored to operational use cases. Implement predictive and prescriptive models to address a wide range of business challenges in manufacturing, supply chain, customer operations, finance, and more. Work with cross-functional teams to define new process automation and optimization opportunities driven by machine learning and intelligent agents. Maintain and enhance internal data systems, ensuring ongoing data integrity, reliability, and governance. Participate in code reviews, model reviews, and best‑practice development, contributing to a high-performance Data Science culture. Strong expertise in data modeling, database design, feature engineering, and data mining with large-scale structured and unstructured datasets. Proficiency with SQL, statistical programming languages (Python, R) and experience building end-to-end ML workflows. Hands-on experience with machine learning algorithms and frameworks, including clustering, decision trees, ensemble methods, deep learning, and reinforcement learning. Familiarity with agentic AI frameworks, autonomous decision agents, and LLM-based workflow orchestration (e.g., tools, function-calling, planning agents). Experience with Tableau or similar BI tools, enabling clear, actionable data storytelling. Expertise in statistical techniques including regression, distribution properties, hypothesis testing, and experimental design. Strong analytical and problem‑solving skills with exceptional attention to detail, data accuracy, and model validation rigor. Ability to systemically identify optimization opportunities and translate them into scalable ML or AI-driven automation solutions. Deep experience writing highly optimized SQL queries and working with large enterprise data systems. Master's Degree in Computer Science, Data Science, Statistics, Engineering, or a related field. 4-7 years of relevant industry experience in applied machine learning, data science, operational analytics, automation, or AI agent development.

Requirements

• Strong expertise in data modeling, database design, feature engineering, and data mining with large-scale structured and unstructured datasets.
• Proficiency with SQL, statistical programming languages (Python, R) and experience building end-to-end ML workflows.
• Hands-on experience with machine learning algorithms and frameworks, including clustering, decision trees, ensemble methods, deep learning, and reinforcement learning.
• Familiarity with agentic AI frameworks, autonomous decision agents, and LLM-based workflow orchestration (e.g., tools, function-calling, planning agents).
• Experience with Tableau or similar BI tools, enabling clear, actionable data storytelling.
• Expertise in statistical techniques including regression, distribution properties, hypothesis testing, and experimental design.
• Strong analytical and problem‑solving skills with exceptional attention to detail, data accuracy, and model validation rigor.
• Ability to systemically identify optimization opportunities and translate them into scalable ML or AI-driven automation solutions.
• Deep experience writing highly optimized SQL queries and working with large enterprise data systems.
• Master's Degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
• 4-7 years of relevant industry experience in applied machine learning, data science, operational analytics, automation, or AI agent development.

Responsibilities

• Optimize operational workflows by designing and deploying ML models that improve efficiency, reduce manual effort, and support data‑driven decision-making across business units.
• Develop autonomous, agentic AI systems that can proactively monitor processes, make recommendations, trigger actions, and continuously improve through feedback loops.
• Interpret and analyze complex datasets using advanced statistical and machine learning techniques; communicate insights clearly to technical and non‑technical stakeholders.
• Build scalable data collection and analytics pipelines that enhance data quality, statistical efficiency, and real-time decisioning.
• Develop and validate custom ML models (supervised, unsupervised, time-series, causal inference, etc.) tailored to operational use cases.
• Implement predictive and prescriptive models to address a wide range of business challenges in manufacturing, supply chain, customer operations, finance, and more.
• Work with cross-functional teams to define new process automation and optimization opportunities driven by machine learning and intelligent agents.
• Maintain and enhance internal data systems, ensuring ongoing data integrity, reliability, and governance.
• Participate in code reviews, model reviews, and best‑practice development, contributing to a high-performance Data Science culture.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees