Product Manager, Data Scientist

Who We Are Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology. What We Offer Salary: $100,000.00 - $136,500.00 Location: Santa Clara,CA You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits . Position Overview: We are seeking a Product Manager with a strong emphasis on data science to define and deliver data-driven products, analytical capabilities, and intelligent workflows. This role sits at the intersection of customer needs, business strategy, data, and technology. It offers the opportunity to lead high-impact, cross-functional initiatives that develop end-to-end AI solutions, from problem formulation through production deployment. The Product Manager will translate complex problems into a clear product vision and roadmap, partner closely with data scientists and engineers, and guide solutions from discovery through launch and continuous improvement. The ideal candidate combines product judgment, analytical fluency, and strong cross-functional leadership. They can evaluate when advanced analytics or machine learning is appropriate, establish meaningful success metrics, and communicate technical tradeoffs to both technical and business audiences. Key Responsibilities Develop and communicate a compelling product vision, strategy, and roadmap for data science–enabled products and capabilities. Identify high-value customer and business problems through research, data analysis, stakeholder interviews, and market insight. Translate product opportunities into clear requirements, user stories, acceptance criteria, and measurable outcomes. Partner with data science, data engineering, software engineering, design, and business teams throughout discovery, development, validation, launch, and adoption. Work within cross-functional project teams to develop complex analytical models, algorithms, and automated processes that address challenging business and customer problems. Drive the development of machine learning models across the full lifecycle, from problem definition and initial concept through validation, production deployment, monitoring, and continuous improvement. Apply data mining and machine learning algorithms to generate insights from historical and real-time data for use cases such as material forecasting, demand forecasting, pattern recognition, and related business challenges. Engage with stakeholders to analyze requirements, special requests, and schedules; derive actionable insights; and partner with business units to define actions and the key performance indicators used to measure their impact. Identify opportunities to improve forecast accuracy and provide Service Supply Chain leadership with business insights and additional perspectives that inform planning and decision-making. Architect a centralized Intelligence Hub spanning savings, margin, revenue, returns, and out-of-control (OOC) indicators to provide real-time visibility into product-line health, reduce manual reporting overhead, and help decrease process variability. Build and optimize scalable SQL pipelines in Databricks and Hadoop to unify complex data sources and enable automated, insight-driven Tableau dashboards that support increased service contract renewals. Automate data orchestration by programmatically synchronizing disparate Sales, Operations, and Finance tables into a Databricks gold layer, reducing weekly manual reporting latency by more than four hours. Implement Statistical Process Control (SPC) and alerting logic within dashboards to detect anomalies in process parameters, shifting operations from reactive troubleshooting to proactive decision intelligence. Develop and validate machine learning models to predict wafer defectivity and removal rates, using R to design experiments and analyze sensor telemetry for performance optimization. Guide experimentation, pilots, and validation plans; use evidence to refine product direction and investment decisions. Required Qualifications 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. Ability to communicate effectively with data scientists, engineers, business leaders, customers, and executive stakeholders. Strong prioritization, problem-solving, and decision-making skills in ambiguous environments Preferred Qualifications 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 Technical Skills Programming Languages: Python (Pandas, scikit-learn, PyTorch, TensorFlow), SQL, R (Tidyverse, design of experiments), Java, C/C++, and JavaScript. Machine Learning and AI: Large language models, natural language processing (BERT, NLTK), computer vision, neural networks (CNNs and RNNs), XGBoost, random forests, clustering, support vector machines, Naive Bayes, and Skip-gram. Data Engineering and Cloud: Databricks, Apache Spark (PySpark), Hadoop, MapReduce, ETL, AWS EC2, Google Cloud Platform, Docker, PostgreSQL, MySQL, NoSQL databases, and object-relational mapping. Advanced Analytics: Time-series forecasting, causal inference, hypothesis testing, A/B testing, design of experiments, generalized linear models, dimensionality reduction, and anomaly detection. Tools and Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly, MATLAB, SAS, SPSS, Qualtrics, and Microsoft Excel with VBA. Additional Information Time Type: Full time Employee Type: Assignee / Regular Travel: Yes, 20% of the Time Relocation Eligible: Yes 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. 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 ba