Product Manager, Data Scientist
About The Position
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. The role sits at the intersection of customer needs, business strategy, data, and technology, and leads cross-functional initiatives to develop end-to-end AI solutions from problem formulation through production deployment. The Product Manager translates complex problems into a clear product vision and roadmap, partners with data scientists and engineers, and guides solutions from discovery through launch and continuous improvement. The ideal candidate combines product judgment, analytical fluency, and cross-functional leadership. They can evaluate when advanced analytics or machine learning is appropriate, establish meaningful success metrics, and communicate technical tradeoffs to technical and business audiences. The role includes developing analytical models and machine learning solutions, improving forecasting, building data pipelines and dashboards, and using experimentation and validation to guide product direction.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- Develop and communicate a 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 requirements, user stories, acceptance criteria, and measurable outcomes.
- Partner with data science, data engineering, software engineering, design, and business teams through discovery, development, validation, launch, and adoption.
- Work with cross-functional teams to develop analytical models, algorithms, and automated processes addressing business and customer problems.
- Drive machine learning models through problem definition, concept development, validation, production deployment, monitoring, and continuous improvement.
- Apply data mining and machine learning algorithms to historical and real-time data for material forecasting, demand forecasting, pattern recognition, and related challenges.
- Analyze stakeholder requirements, special requests, and schedules; derive actionable insights and partner with business units to define actions and impact KPIs.
- Identify opportunities to improve forecast accuracy and provide Service Supply Chain leadership with insights to inform planning and decisions.
- Architect a centralized Intelligence Hub covering savings, margin, revenue, returns, and out-of-control indicators to provide real-time product-line visibility, reduce manual reporting, and decrease process variability.
- Build and optimize SQL pipelines in Databricks and Hadoop to unify data sources and enable automated Tableau dashboards supporting service contract renewals.
- Programmatically synchronize Sales, Operations, and Finance tables into a Databricks gold layer to automate orchestration and reduce weekly manual reporting latency by more than four hours.
- Implement Statistical Process Control and dashboard alerting to detect anomalies and enable proactive decision intelligence.
- Develop and validate machine learning models to predict wafer defectivity and removal rates; use R to design experiments and analyze sensor telemetry for performance optimization.
- Guide experimentation, pilots, and validation plans, and use evidence to refine product direction and investment decisions.
Benefits
- Comprehensive benefits package
- Potential eligibility for participation in a bonus program
- Potential eligibility for a stock award program
- Supportive work culture that encourages learning, development, and career growth
- Programs and support for employee health and wellbeing
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
