{"id":172678,"url":"https://alion.io/job/applied-materials-manager-data-scientist","title":"Manager, Data Scientist","company":{"id":3903,"name":"Applied Materials","domain":"appliedmaterials.com","url":"https://alion.io/company/amat","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":89,"open_postings":327,"ghost_share":0.003,"stale_share":0.413,"repost_share":0.003,"time_to_fill_p50_days":52,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":176000,"max":242000,"currency":"USD","period":"year","gross":null,"usd_annual":242000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-07-10T00:00:00Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-18T09:04:12Z","board_verified":true,"closed_at":null,"days_open":100,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":100},"description":"Who We Are\nApplied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.\nWhat We Offer\nSalary:\n$176,000.00 - $242,000.00Location:\nAustin,TXYou’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.\nAt Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.\nKey Responsibilities\nLead the architecture, design, and implementation of Agentic AI solutions and Multi-Agent Systems that solve complex business and manufacturing challenges through autonomous reasoning, planning, orchestration, and execution.\nDrive the development of AI agents using modern frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents, Azure AI Foundry) to enable decision intelligence, workflow automation, knowledge retrieval, and operational optimization.\nServe as a hands-on technical leader responsible for building scalable AI platforms, including agent orchestration, memory management, tool integration, Retrieval-Augmented Generation (RAG), knowledge graphs, ontologies, and enterprise AI architectures.\nLead the development of advanced AI capabilities, including reasoning agents, planning agents, orchestration agents, code-generation agents, analytics agents, and domain-specific copilots that improve business outcomes and operational efficiency.\nCollaborate with business stakeholders, product teams, engineers, data scientists, and subject matter experts to identify high-value AI use cases and translate them into production-grade AI solutions.\nEstablish AI engineering best practices covering LLMOps, AI governance, evaluation frameworks, observability, security, safety, prompt engineering, context engineering, model optimization, and continuous improvement.\nArchitect and develop enterprise AI platforms leveraging Azure AI, Databricks, Python, vector databases, graph databases, cloud-native technologies, and modern machine learning frameworks.\nBuild and optimize agent memory architectures, semantic layers, knowledge repositories, and enterprise ontologies to improve reasoning quality, contextual awareness, and autonomous execution.\nLead proof-of-concept development, rapid prototyping, and production deployments while ensuring scalability, reliability, maintainability, and measurable business value.\nMentor and guide AI engineers and data scientists while remaining actively involved in coding, architecture reviews, solution design, model development, and technical problem solving.\nStay current with emerging advances in Generative AI, Agentic AI, foundation models, reasoning systems, and autonomous agents, driving adoption of innovative technologies across the organization.\nRequired Education Background\nBachelors in one of the following Computer Science, Artificial Intelligence or Data Science\nFunctional Knowledge\nRecognized technical expert in Generative AI, Agentic AI, Multi-Agent Architectures, and Enterprise AI Platforms.\nDeep expertise in Large Language Models (LLMs), RAG, vector databases, knowledge graphs, AI orchestration frameworks, machine learning, and cloud-native architectures.\nStrong hands-on software engineering capabilities with Python and modern AI development frameworks.\nDemonstrated ability to design scalable, production-ready AI systems across multiple technology domains.\nBusiness Expertise\nAnticipates emerging AI technology trends and identifies opportunities to create competitive advantages through AI-driven automation and intelligence.\nPartners with business leaders to define AI strategy, prioritize use cases, and deliver measurable business outcomes through autonomous and intelligent systems.\nUnderstands manufacturing, supply chain, engineering, operational, and enterprise business processes and how Agentic AI can transform them.\nLeadership\nLeads complex AI transformation initiatives from strategy through implementation and production deployment.\nDrives cross-functional teams delivering enterprise-scale Agentic AI and automation solutions.\nInfluences technical direction, architecture standards, and AI governance across the organization.\nProblem Solving\nSolves highly complex and ambiguous business and technical problems through innovative application of AI, machine learning, and autonomous agent technologies.\nDevelops novel approaches for reasoning, planning, orchestration, workflow automation, and knowledge-driven decision making.\nBalances experimentation and innovation with production-grade engineering principles.\nImpact\nInfluences enterprise AI strategy, technology investments, architecture decisions, and adoption of next-generation AI capabilities.\nDelivers scalable AI solutions that improve productivity, operational performance, decision quality, and business agility.\nShapes long-term AI platform roadmaps and standards for the organization.\nInterpersonal Skills\nCommunicates complex AI concepts and architectures effectively to executive leadership, technical teams, and business stakeholders.\nDrives alignment across diverse organizations and builds consensus around AI strategy and solution approaches.\nEffectively mentors teams and promotes adoption of AI best practices across the enterprise.\nPreferred Qualifications\n7+ years of software engineering, data science, machine learning, or AI experience.\n3+ years building production-grade AI/ML solutions using Python.\n3+ years developing Generative AI, Agentic AI, Multi-Agent Systems, RAG, Knowledge Graphs, or LLM applications.\nExperience with Azure AI, Databricks, OpenAI, LangGraph, Semantic Kernel, CrewAI, AutoGen, vector databases, and cloud-native architectures.\nDemonstrated track record delivering enterprise-scale AI products from concept through production.\n\nAdditional Information\nTime Type:\nFull timeEmployee Type:\nAssignee / RegularTravel:\nYes, 20% of the TimeRelocation Eligible:\nYesThe salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.\nFor all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.\nApplied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.\nIn addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at ,or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. 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