Applied Materials

Data Scientist I New College Grad- Bachelor's (Santa Clara, CA)

Santa Clara, CA US
USD 92k - 126k
Python R SQL Machine Learning TensorFlow
Description

Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale helps our customers – who make smartphones, supercomputers, virtual reality headsets, autonomous vehicles and more – transform their ideas into reality. Our innovations Make Possible® a Better Future.

Inside our company, we apply the idea of make possible as we work together. We value our people and teams who turn possibilities into reality by advancing our strategy, accomplishing great things and empowering others. We are deeply committed to fostering a Culture of Inclusion where every person knows they belong, feels empowered to bring their whole self to work, and is inspired to grow.

We are looking for a Data Scientist to join our AGS BPIx team. The mission of the AGS BSA BPIx team is to look at the process gaps, identify the problems, recommend the to-be process, and provide business actionable insights. Our data science modeling and analytical efforts focus on improving process efficiency through insights, enhanced visualization, and improved UI/UX. We aim to elevate our decision support systems and measurement capabilities, driving significant improvements in key business metrics.

TEAM OVERVIEW:

The AGS BPIx team is dedicated to thoroughly examining and identifying inefficiencies and gaps within existing processes. We take a proactive approach to understanding the root causes of these issues and work collaboratively to develop and recommend optimized solutions that streamline operations and enhance overall efficiency. Our goal is to transform data into actionable business insights that drive informed decision-making and strategic improvements. By leveraging advanced data science techniques, we aim to provide valuable recommendations that not only address current challenges but also anticipate future needs, ensuring sustained business growth and success.

KEY RESPONSIBILITIES:

  • Develop a comprehensive framework using Statistical Process Control (SPC) techniques to monitor and control various metrics. Highlight when metrics fall outside the acceptable range (Lower Control Limit and Upper Control Limit), enabling early detection of potential issues in field service and sales processes. Ensure that any deviations are quickly identified and addressed, maintaining smooth operations.
  • Continuously monitor field data to detect anomalies that could impact productivity. Analyze these anomalies to understand their root causes and recommend corrective actions. Improve overall productivity and increase contract margins by resolving issues before they escalate.
  • Use advanced data analysis and predictive modeling techniques to identify patterns and trends in field service and sales processes. Predict potential anomalies before they occur, allowing the team to take preventive measures to maintain smooth operations. Minimize disruptions and ensure that the business runs smoothly.
  • Create and refine predictive models to forecast headcount needs and optimize the QRAM (Quarterly Resource Allocation Meeting) model to better predict resource allocation. Ensure that resources are used effectively and efficiently, supporting the organization's strategic goals.
  • Conduct thorough data analysis to uncover valuable insights. Use statistical methods and data visualization tools to interpret data and present findings in a clear and actionable manner. Optimize processes and improve overall efficiency, leading to better business outcomes.

TECHNICAL SKILLS:

  • Programming Languages: Proficiency in Python or R for data analysis and model development.
  • Data Manipulation: Strong skills in SQL for querying databases and manipulating data sets.
  • Machine Learning: Familiarity with machine learning algorithms and frameworks such as TensorFlow to build predictive models.
  • Statistical Analysis: Solid understanding of statistical methods and techniques to analyze data and interpret results.
  • Data Visualization: Hands on experience with visualization tools like Tableau.
  • Predictive Modeling: Ability to develop, validate, and deploy predictive models to forecast trends and anomalies.
  • Problem-Solving: Excellent analytical and problem-solving skills to identify issues, develop solutions, and optimize processes.
  • Communication: Strong communication skills to effectively present findings, collaborate with team members

REQUIREMENTS/EDUCATION:

  • Bachelor's degree in Data Science, Data Analytics, or related field
  • GPA of 3.0 or above preferred

Qualifications

Education:

Bachelor's Degree (Required)

Skills:

Certifications:

Languages:

Years of Experience:

Up to 1 Year

Work Experience:

Additional Information

Time Type:

Full time

Employee Type:

New College Grad

Travel:

No

Relocation Eligible:

Yes

U.S. Salary Range:

$92,000.00 - $126,500.00

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. 

Applied Materials
Applied Materials
Electronics Manufacturing Semiconductor Software

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