US_Data Analyst_Professional

Overview

Are you an experienced data analyst with a passion for using data to identify insights and drive decisions that improve product quality?

This role offers the opportunity to be part of a dynamic Quality and Customer Satisfaction team focused on leveraging advanced analytics and machine learning to detect emerging issues, define problems, and support resolution efforts. You will collaborate with global teams and contribute to improving product performance and customer experience.

The team is responsible for monitoring product quality performance across the full product lifecycle by investigating and prioritizing field issues. Data insights play a critical role in amplifying the voice of the customer through clear, impactful storytelling and supporting data‑driven decision‑making processes.

In this position, you will apply both fundamental and advanced analytics techniques to accelerate issue identification and resolution. The role involves working across multiple product technologies and collaborating with cross‑functional teams in a global environment.

• Execute end-to-end analytics projects, including data exploration, structuring, modeling, problem‑solving, and delivering recommendations
• Develop and implement methodologies to support early issue detection, forecasting, and performance prediction
• Analyze data from multiple sources such as warranty, equipment logs, customer feedback, and manufacturing data
• Contribute to improving product uptime and overall quality performance
• Work independently while also building strong cross‑functional collaboration with global teams
• Partner with data analysts, engineers, architects, and data scientists to design and deploy advanced analytics solutions

• Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or a related field (Master’s preferred)
• Experience solving technical problems in a quality‑focused environment
• Strong proficiency in Power BI
• Hands‑on experience with tools and technologies such as SQL, Python, R, cloud analytics platforms, Jupyter Notebook, and Apache Spark
• Working knowledge of machine learning techniques (supervised and unsupervised), including algorithms such as clustering, regression, classification, and anomaly detection

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Location: Greensboro