Product Manager, Data Scientist

Build end-to-end AI solutions, Databricks pipelines, and Statistical Process Control models for wafer defectivity prediction.

Originally posted 27 October 2026 by the employer.

About the role

Applied Materials is seeking a Product Manager with a strong emphasis on data science to define and deliver data-driven products, analytical capabilities, and intelligent workflows. In this role, you will lead initiatives developing end-to-end AI solutions spanning machine learning models for wafer defectivity, material forecasting, and operational dashboards.

What you'll do

• Develop and communicate product vision, strategy, and roadmaps for data science-enabled products and capabilities.
• Translate business and customer opportunities into clear requirements, user stories, acceptance criteria, and measurable outcomes.
• Drive machine learning model development across the full lifecycle, from problem formulation and concept through validation, deployment, monitoring, and continuous improvement.
• Architect an Intelligence Hub and build scalable SQL pipelines in Databricks and Hadoop to power automated Tableau dashboards.
• Implement Statistical Process Control (SPC) and alerting logic within dashboards to detect anomalies in process parameters.
• Develop and validate machine learning models to predict wafer defectivity and removal rates, using R to design experiments and analyze sensor telemetry.

What you'll need

• Bachelor’s degree in a quantitative, technical, business, or related field, or equivalent practical experience.
• Working knowledge of the data science lifecycle, including data preparation, feature development, experimentation, model evaluation, deployment, and monitoring.
• Strong analytical skills and experience using data to frame problems, evaluate options, and measure outcomes.
• Demonstrated ability to communicate technical tradeoffs with data scientists, engineers, business leaders, and executive stakeholders.
• Prioritization and decision-making skills in ambiguous environments.

Nice to have

• Advanced degree in data science, computer science, statistics, engineering, operations research, business, or a related discipline.
• Experience with enterprise, industrial, manufacturing, or other technically complex products and workflows.
• Familiarity with SQL, Python, business intelligence tools, cloud data platforms, MLOps, or model monitoring practices.
• Experience taking machine learning or advanced analytics solutions from prototype to production.

Skills: product management, machine learning, data science, statistical process control, Databricks

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