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This listing doesn't post a salary. Most similar roles pay
$172,300–$226,800.
Based on 240 similar postings.
Employer
About Applied Materials
Applied Materials is the world''s largest supplier of equipment, services, and software for the semiconductor and display industries, enabling the production of chips and advanced displays. Industry: Semiconductor Equipment
Applied Materials currently has
344 open roles
on FindRole.
Listed pay typically runs
$124,000–$171,000
across 217 roles with salary data.
JOB TITLE: Product Manager, Data Scientist
The Product Manager, Data Scientist joins the team to define and deliver data-driven products, analytical capabilities, and intelligent workflows. This role sits at the intersection of customer needs, business strategy, and technology, leading cross-functional initiatives to develop end-to-end AI solutions from problem formulation through production deployment. Responsibilities include developing product visions, translating opportunities into requirements, and driving the full machine learning lifecycle. The role involves building scalable SQL pipelines, automating data orchestration, and implementing Statistical Process Control to detect anomalies. Key technologies include Python, SQL, R, Databricks, Hadoop, and Tableau. The position addresses complex business problems such as material forecasting, demand forecasting, and predicting wafer defectivity by applying data mining, neural networks, and time-series forecasting to improve operational decision-making and process variability.
Develop and communicate a product vision, strategy, and roadmap for data science-enabled products.
Identify high-value business problems through research, data analysis, and stakeholder interviews.
Translate product opportunities into clear requirements, user stories, and measurable outcomes.
Drive the development of machine learning models from initial concept through production deployment and monitoring.
Build and optimize scalable SQL pipelines in Databricks and Hadoop to unify complex data sources.
Automate data orchestration by synchronizing disparate Sales, Operations, and Finance tables into a gold layer.
Implement Statistical Process Control and alerting logic within dashboards to detect process anomalies.
Develop and validate machine learning models to predict wafer defectivity and removal rates.
What we're looking for
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.
Advanced degree in data science, computer science, statistics, engineering, operations research, business, or a related discipline (preferred).
Experience with enterprise, industrial, manufacturing, or other technically complex products and workflows (preferred).
Familiarity with SQL, Python, business intelligence tools, cloud data platforms, MLOps, or model monitoring practices (preferred).
Python
SQL
Tableau
Business Intelligence
Data Visualization
Databases
AI
Data Strategy
Prototyping
Supply Chain Management
Manufacturing Operations
NPI
Process Improvement
Master Data Management (MDM)
Data Governance
Data Quality
Metadata Management
Generative AI
ChatGPT
Microsoft Copilot
APIs
Swagger
OpenAPI
Event-driven Architecture
Agile
Product Roadmap
Data Services
GCP
BigQuery
Looker
Medallion Architecture
Data Governance
Data Modeling
Dimensional Modeling
AI
ML
Data Strategy
Data Architecture
Schema Design
Predictive Automation
Anomaly Detection
Forecasting