Data Scientist New College Grad- Bachelor's/Master's (Austin, TX)

TEAM OVERVIEW: The Logistics Data Management (LDM) organization enables data-driven logistics operations across the global supply chain through Supplier Connectivity, Freight Audit & Payment, Transportation Management System (TMS) Optimization, and Data & Analytics platforms. This position will be part of the Logistics Data Repository (LDR) team, which serves as the central data and analytics platform for logistics operations. LDR integrates data from transportation, ERP, and other enterprise systems, transforming it into a unified data foundation that supports reporting, analytics, operational visibility, AI-driven initiatives, and business decision-making. KEY RESPONSIBILITIES: * Develop and support ETL/ELT pipelines using Python, SQL, PySpark, and modern data engineering frameworks. * Build, test, and optimize data pipelines and analytics-ready datasets on cloud data platforms. * Perform data analysis and exploratory data investigations to identify trends, anomalies, and business insights. * Support analytics, reporting, and machine learning initiatives by preparing and validating high-quality data. * Collaborate with Data Engineers, Data Scientists, and business stakeholders to solve complex business problems through data-driven solutions. * Support AI-driven and intelligent automation initiatives and contribute to identifying opportunities where AI can improve business processes and engineering productivity. * Assist with data quality, monitoring, troubleshooting, and operational support activities. * Participate in design discussions, code reviews, and continuous improvement initiatives. TECHNICAL SKILLS: * Strong foundation in Python and SQL * Understanding of Data Structures and Algorithms * Exposure to PySpark, Apache Spark, or distributed data processing frameworks * Knowledge of Data Engineering, ETL/ELT, and Data Warehousing concepts * Familiarity with cloud platforms such as Azure, AWS, or GCP * Exposure to Databricks, Airflow, or similar data engineering tools is preferred * Understanding of data analysis, statistics, and machine learning fundamentals * Experience with Pandas, NumPy, or similar data-processing libraries * Familiarity with data visualization tools such as Power BI or Tableau is a plus * Exposure to Generative AI, LLMs, or AI-assisted development tools is desirable REQUIREMENTS/EDUCATION: * Bachelors or Masters degree in Computer Science, Data Science, Information Systems, Software Engineering, Computer Engineering, Artificial Intelligence, Machine Learning, or related field * GPA of 3.0 or above preferred * Strong analytical, problem-solving, communication, and teamwork skills with a demonstrated passion for working with data and technology. 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: Bachelor's Degree (Required) ### Skills ### Certifications: ### Languages: ### Years of Experience: 1 - 2 Years ### Work Experience: ## Additional Information ### ### Shift: 10-Day 8-Hr (United States of America) ### ### Travel: Yes, 10% of the Time ### ### Relocation Eligible: No ### Referral Payment Plan: None Salary Range: $80,000.00 - $110,000.00 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.