Data Scientist- Hardware Quality; Hybrid

The application window is expected to close on: 07/27/2026

Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received. This is a hybrid role based twice a week in the Cisco San Jose Office.

We are part of Cisco’s Supply Chain Operations. You will join our Hardware Quality team, a diverse group of quality and failure analysis engineers who collaborate closely with our design, manufacturing, supplier, and customer engagement teams. In this role, you will transform complex data into actionable insights, empowering our hardware teams to anticipate and meet the evolving quality expectations of our customers.

As a Quality Data Scientist at Cisco, you will be instrumental in transforming complex data into strategic insights that drive business decisions. This role uniquely combines quality domain expertise with advanced data analytics capabilities. We are seeking a professional with hands‑on quality experience who can bridge the gap across quality operations and management processes to data science. The ideal candidate understands quality processes from the ground up and can translate quality requirements into actionable data strategies, not just someone who analyzes data in isolation.

Success in this role combines not only understanding the data and advanced analytics but also combining it with exceptional communication skills crafting compelling data stories and executive messaging.

• Leverage quality domain knowledge to identify critical quality indicators and translate them into measurable data requirements and analytical frameworks.
• Conduct advanced statistical analyses and correlation studies to uncover meaningful patterns and relationships in large datasets.
• Connect disparate quality metrics across the product lifecycle to create holistic views that reveal systemic patterns and root causes.
• Bridge quality operations and data analytics by understanding end-to-end quality processes, identifying data gaps, and designing automated solutions that address real-world quality challenges.
• Develop and refine predictive models and analytical frameworks to support business initiatives.
• Design and implement automated data pipelines and monitoring systems that proactively detect quality issues and trigger preventive actions.
• Collaborate with cross-functional teams to integrate diverse data sources and ensure data integrity.
• Translate complex analytical findings into clear, persuasive narratives tailored for executive audiences.
• Design and deliver impactful visualizations and presentations that align data insights with organizational goals.
• Partner with quality engineers and operations teams to understand their challenges firsthand and develop data-driven automation solutions that enhance efficiency.
• Continuously improve data workflows and analytical methodologies to enhance efficiency and precision.

• Statistics: Deep understanding of statistical methods, hypothesis testing, and data modeling techniques.
• Quality Management Processes: Experience with product quality management
• Correlation Analysis: Proficient in identifying and interpreting relationships between variables to inform decision-making.
• Communication and Storytelling: Skilled in crafting data-driven stories that effectively engage and influence executive leadership.
• Executive Messaging: Experience in developing concise, strategic presentations and reports for senior stakeholders.

• This role is critical to Cisco’s mission of leveraging data to drive innovation and business growth, requiring a blend of analytical rigor, quality focus, and exceptional communication skills to deliver impactful outcomes.

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
• 7+ years of professional experience in data science, analytics, or a related discipline, with demonstrated expertise in statistical analysis and quality management.
• Proven track record of applying correlation analysis and advanced statistical techniques in a business context.
• Experience working with executive…