Data Scientist & Data Analyst

Applied Materials•Santa Clara, CA

23h•$124,000 - $171,000

About The Position

The EPIC Center’s success depends on high‑quality operational data, unified data models, measurable velocity metrics, and advanced analytics that accelerate R&D and lab execution. We are seeking a Data Scientist & Data Analyst to lead the development of EPIC’s operational data foundation and deliver insights that enable measurable improvements in lab productivity, experiment throughput, resource efficiency, and end‑to‑end process velocity.

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or related field.
3–7 years of experience in data analytics, data science, or operational analytics, preferably in a technical or R&D environment.
Strong skills in data modeling, statistical analysis, and translating operational workflows into structured datasets.
Proficiency with data analysis tools (Python, R, SQL) and visualization platforms.
Experience building dashboards, models, and analytics that support operations, productivity, or process optimization.
Ability to communicate insights clearly to technical and non‑technical audiences.

Nice To Haves

Semiconductor industry experience preferred.
Familiarity with lab environments, experiment workflows, or operational data challenges is a strong plus.

Responsibilities

Operational Data Foundation & Modeling Develop and maintain conceptual, logical, and operational data models for lab operations, experiments, equipment, materials, metrology, and supporting workflows.
Identify data gaps and fragmentation across systems, workflows, and sources; propose solutions to improve data completeness, consistency, and quality.
Define and refine threading keys required for end‑to‑end visibility and velocity measurement across lab processes.
Build structured datasets that enable measurement of cycle time, productivity, utilization, and other operational KPIs.
Analytics, Insights & Decision Support Analyze operational workflows and experiment cycles to identify bottlenecks, inefficiencies, and opportunities for acceleration.
Build dashboards, reports, and analytical tools that provide real‑time visibility into lab performance and velocity metrics.
Apply data science, statistical modeling, and predictive analytics to support continuous improvement and scenario analysis.
Support leaders with insights that influence strategic decisions, resource planning, and operational design.
AI‑Enabled Lab Operations & Automation Contribute to development of AI‑assisted operational workflows, including agents for optimization and automated insights.
Identify opportunities for automation and machine‑learning‑driven recommendations across lab operations.
Cross‑Functional Collaboration Work closely with lab operations, engineering, R&D, logistics, facilities, and system owners to understand data needs and operational context.
Collaborate with information system owners and technical teams to ensure analytics requirements shape data architecture and system design.
Partner with transformation and business process teams to integrate analytics into redesign efforts.
Measurement Frameworks & Velocity Metrics Define and maintain frameworks that measure velocity, productivity, and throughput across lab processes.
Develop methods to quantify improvements from system changes, workflow redesign, and operational interventions.
Support creation of baseline measurement dashboards and KPIs for EPIC workflows.