Interested in this Data Scientist role at Applied Materials?
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About This Role
Who We Are
Applied 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.
What We Offer
Salary:
$176,000\.00 \- $242,000\.00
Location:
Austin,TX
You’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.
At 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 .
- Key Responsibilities
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Lead 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.
Drive 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.
Serve 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.
Lead 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.
Collaborate 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.
Establish AI engineering best practices covering LLMOps, AI governance, evaluation frameworks, observability, security, safety, prompt engineering, context engineering, model optimization, and continuous improvement.
Architect and develop enterprise AI platforms leveraging Azure AI, Databricks, Python, vector databases, graph databases, cloud\-native technologies, and modern machine learning frameworks.
Build and optimize agent memory architectures, semantic layers, knowledge repositories, and enterprise ontologies to improve reasoning quality, contextual awareness, and autonomous execution.
Lead proof\-of\-concept development, rapid prototyping, and production deployments while ensuring scalability, reliability, maintainability, and measurable business value.
Mentor and guide AI engineers and data scientists while remaining actively involved in coding, architecture reviews, solution design, model development, and technical problem solving.
Stay current with emerging advances in Generative AI, Agentic AI, foundation models, reasoning systems, and autonomous agents, driving adoption of innovative technologies across the organization.
Required Education Background
Bachelors in one of the following Computer Science, Artificial Intelligence or Data Science
Functional Knowledge
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+ Recognized technical expert in Generative AI, Agentic AI, Multi\-Agent Architectures, and Enterprise AI Platforms .
+ Deep expertise in Large Language Models (LLMs), RAG, vector databases, knowledge graphs, AI orchestration frameworks, machine learning, and cloud\-native architectures.
+ Strong hands\-on software engineering capabilities with Python and modern AI development frameworks.
+ Demonstrated ability to design scalable, production\-ready AI systems across multiple technology domains.Business Expertise
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+ Anticipates emerging AI technology trends and identifies opportunities to create competitive advantages through AI\-driven automation and intelligence.
+ Partners with business leaders to define AI strategy, prioritize use cases, and deliver measurable business outcomes through autonomous and intelligent systems.
+ Understands manufacturing, supply chain, engineering, operational, and enterprise business processes and how Agentic AI can transform them.Leadership
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+ Leads complex AI transformation initiatives from strategy through implementation and production deployment.
+ Drives cross\-functional teams delivering enterprise\-scale Agentic AI and automation solutions.
+ Influences technical direction, architecture standards, and AI governance across the organization.Problem Solving
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+ Solves highly complex and ambiguous business and technical problems through innovative application of AI, machine learning, and autonomous agent technologies.
+ Develops novel approaches for reasoning, planning, orchestration, workflow automation, and knowledge\-driven decision making.
+ Balances experimentation and innovation with production\-grade engineering principles.Impact
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+ Influences enterprise AI strategy, technology investments, architecture decisions, and adoption of next\-generation AI capabilities.
+ Delivers scalable AI solutions that improve productivity, operational performance, decision quality, and business agility.
+ Shapes long\-term AI platform roadmaps and standards for the organization.Interpersonal Skills
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+ Communicates complex AI concepts and architectures effectively to executive leadership, technical teams, and business stakeholders.
+ Drives alignment across diverse organizations and builds consensus around AI strategy and solution approaches.
+ Effectively mentors teams and promotes adoption of AI best practices across the enterprise.Preferred Qualifications
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+ 7\+ years of software engineering, data science, machine learning, or AI experience.
+ 3\+ years building production\-grade AI/ML solutions using Python.
+ 3\+ years developing Generative AI, Agentic AI, Multi\-Agent Systems, RAG, Knowledge Graphs, or LLM applications.
+ Experience with Azure AI, Databricks, OpenAI, LangGraph, Semantic Kernel, CrewAI, AutoGen, vector databases, and cloud\-native architectures.
+ Demonstrated track record delivering enterprise\-scale AI products from concept through production.
Additional Information
Time Type:
Full time
Employee Type:
Assignee / Regular
Travel:
Yes, 20% of the Time
Relocation Eligible:
Yes
The 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.
For 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.
Applied 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.
In 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 Accommodations\[email protected], or by calling our HR Direct Help Line at 877\-612\-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $176K-$242K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Applied Materials, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($209K) sits 8% above the category median. Disclosed range: $176K to $242K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Applied Materials AI Hiring
Applied Materials has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Santa Clara, CA, US, Austin, TX, US. Compensation range: $234K - $253K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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