Data Scientist II, New College Grad- Master's (Santa Clara, CA)

TEAM OVERVIEW

Join our dynamic Supply Chain Analytics team as a Data Scientist! Our mission is to revolutionize efficiency by harnessing the power of machine learning, statistical and simulation modeling, generative AI, and actionable insights.

We are dedicated to transforming process efficiency through deep analysis, cutting-edge visualization, and enhanced UI/UX. Our goal is to elevate decision support systems and measurement capabilities, driving substantial improvements in key business metrics. Be a part of our journey to make a significant impact!

HOW WE WORK

AI has changed how fast a data scientist can move. It has not changed what makes one good.

We look for people who use AI tools aggressively to move faster, and who can still explain every line they ship, defend the assumptions behind a model, and recognize an answer that is fluent but wrong. Strong fundamentals first, AI as a multiplier.

KEY RESPONSIBILITIES

• Design, develop, and deploy AI solutions - search, summarization, agentic automation, AI-assisted decision support - built as reusable components and workflows that scale across use cases.
• Build the data pipelines that analysis and AI solutions depend on.
• Take work from notebook to production, with documented methods, data lineage, and clear ownership.
• Build statistical and machine learning models, and evaluate them honestly, including where they fail.
• Partner with cross-functional stakeholders to turn ambiguous problems into structured solutions with measurable outcomes.
• Build dashboards and applications in Tableau and Power BI that decision makers actually use, and present findings to business and executive audiences.

TECHNICAL SKILLS

• Understanding of supply chain management concepts (planning, inventory, logistics, procurement, repair operations, reverse value chain).
• Strong database fundamentals (RDBMS concepts, relational and dimensional data modeling, normalization, keys and constraints, indexing, query execution plans).
• Strong SQL skills (joins, aggregations, subqueries, CTEs, window functions, query optimization).
• Big data fundamentals (distributed processing, partitioning, columnar and table formats, Spark and lakehouse architecture).
• Data engineering fundamentals (ETL/ELT, ingestion from multiple source systems, pipeline design and orchestration, data quality and validation).
• Strong Python skills for data analysis and machine learning (Pandas, NumPy, SciPy, scikit-learn).
• Machine learning fundamentals (supervised and unsupervised methods, feature engineering, cross-validation, evaluation metrics, overfitting and regularization).
• Statistical fundamentals (distributions, sampling, hypothesis testing, experimental design, correlation versus causation).
• Hands-on experience with Databricks or a comparable cloud data platform (Snowflake, BigQuery).
• Data visualization with Tableau and/or Power BI.

Applied and AI skills

• Applied GenAI and LLM experience (prompting, retrieval augmented generation, LLM APIs, agentic workflows, output evaluation).
• Knowledge of Six Sigma, Lean, or DMAIC process improvement methodologies.

REQUIREMENTS / EDUCATION

• Master's degree in Data Science, Operations Research, Engineering, Computer Science, Statistics, Supply Chain Analytics, or another quantitative field.
• GPA of 3.0 or above preferred.
• Demonstrated skills in data science and business analytics.

IDEAL CANDIDATE

The ideal candidate takes ownership of a problem, exercises judgment about what is worth building, and sees it through to adoption. This role is equally technical and collaborative, and strong technical skills alone will not carry the work.

You are someone who:

• Investigates the data instead of accepting it, and asks the question behind the question.
• Traces a number end to end - from the business process that generated it, to the table it lands in, to the decision it drives.
• Treats stakeholders as partners in the problem rather than submitters of requests, and pushes back when the request will not solve it.
• Builds working relationships across functions and time zones, and keeps partners informed as the work progresses.
• Sees the pattern across two problems that looked unrelated, and builds the thing that solves both.
• Cares whether the solution actually gets used, not just whether it works.

ADDED ADVANTAGE

• Knowledge of supply chain circular economy and reverse value chain processes.
• Machine learning or AI solutions taken end to end, from data preparation through deployment.
• Production LLM application work (RAG pipelines, agents, or evaluation harnesses).
• Experience with Databricks ETL, data ingestion, or BI and AI workloads on a governed data platform.

Applications will be reviewed on a rolling basis. Please apply by November 30, 2026. Note: This position may close early based on application volume or candidate selection.

## Qualifications

### Education:

Master's Degree (Required)

### Skills

### Certifications:

### Languages:

### Years of Experience:

2 - 4 Years

### Work Experience:

## Additional Information

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### Shift:

10-Day 8-Hr (United States of America)

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### Travel:

Yes, 10% of the Time

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### Relocation Eligible:

Yes

### Referral Payment Plan:

None

Salary Range:

$119,500.00 - $164,500.00

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 committed to diversity in the workplace. All 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.