Data Scientist - Agentic AI / ML

Applied Materials
Santa Clara
Workplace: OnsiteFull timeUSD 142,500 - 196,500 annuallyFunction: Data Science & Machine Learning0Education: mastersSkills: ["Analytical and problem-solving abilities","Communication","Storytelling","Collaboration","Influencing executive stakeholders"]

Develop and deploy advanced machine learning solutions for supply chain and service parts operations, including GenAI and LLM projects. Build and fine-tune agentic AI capabilities such as RAG and embedding-based search, create AI-driven prototypes, and collaborate with stakeholders to translate requirements into KPIs. Partner across cross-functional teams to improve forecast accuracy and deliver production-ready models with MLOps and monitoring.

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FursaFursa
Applied Materials
Applied Materials
5 days ago

Data Scientist - Agentic AI / ML

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Last checked: 2 days agoStatus: Live
Reposted: similar role first listed 3 months ago

Job Summary

Develop and deploy advanced machine learning solutions for supply chain and service parts operations, including GenAI and LLM projects. Build and fine-tune agentic AI capabilities such as RAG and embedding-based search, create AI-driven prototypes, and collaborate with stakeholders to translate requirements into KPIs. Partner across cross-functional teams to improve forecast accuracy and deliver production-ready models with MLOps and monitoring.
Location: Santa Clara
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Entry level

Key Responsibilities

  • •Develop and deploy difficult analytical models, algorithms, and automated processes from inception through production.
  • •Work on GenAI and LLM projects including fine-tuning models, building embedding-based search systems, and developing AI-driven business operation prototypes.
  • •Use data mining and machine learning to derive insights from historical and real-time data for forecasting, pattern recognition, and related initiatives.
  • •Interface with stakeholders for requirements analysis and special requests; derive insights and partner with business units to determine actions and KPIs.
  • •Collaborate across multiple teams (material planning, field operations, inventory management, NPI, production demand planning, reliability engineering, and service campaigns) to improve forecast accuracy and provide insights to service supply chain leadership.
Travel: Low travel

Pay and Benefits

Salary: USD 142,500 - 196,500 annually
Equity and Bonus:Equity

Key Requirements

  • •Advanced degree (MS or PhD) in a quantitative field such as Statistics, Computer Science, Economics, or Operations Research.
  • •0-2 years of industry experience in applied machine learning or related AI work.
  • •Hands-on experience building GenAI-focused applications (e.g., agents, reasoning workflows, or RAG) and understanding how LLMs are architected and operated.
  • •Hands-on experience with Deep Learning frameworks such as PyTorch, Jax, or TensorFlow.
  • •Proficiency in Python and SQL, plus MLOps experience (model deployment, versioning, and performance monitoring).
Experience:0Machine learningAIGenAILLMMLOps
Education:Master's in Quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research)
Skills:Analytical and problem-solving abilitiesCommunicationStorytellingCollaborationInfluencing executive stakeholders
Tech Stack:PythonSQLScikit-learnPyTorchJaxTensorFlowMLOpsModel deploymentModel versioningPerformance monitoringGenAILLMsAgentsReasoning workflowsRAGEmbedding-based searchData miningForecasting librariesGitLinux

Company Brief

Applied Materials
Provides semiconductor manufacturing equipment, services, and software used to build advanced chips and display technologies. Its tools support materials engineering, deposition, etching, inspection, and process control for major electronics manufacturers worldwide.
Industry: Industrial Machinery
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1967
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Glassdoor: 3.9
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