Data Analyst & Master Data Coordinator

GE HealthCare

2d•Onsite

About The Position

The Supply Chain Data Analyst will support the RadioPharma Operations team by delivering reliable reporting, dashboards, and analytical insights that improve supply chain performance, network reliability, and patient access across GE HealthCare’s USCAN PET radiopharmaceutical supply chain. The role sits within RadioPharma Operations and works under the direction of the Supply Chain Data Scientist, supporting broader analytics initiatives by owning recurring reporting, KPI tracking, and foundational operational analyses. The Data Analyst will partner closely with operations, manufacturing, external manufacturing, commercial, and quality teams to convert operational data into clear, actionable insights. This position supports a highly time-sensitive supply network, including cyclotron manufacturing, radiopharmacy operations, and logistics for PET radiotracers with short half-lives. The successful candidate will bring strong analytical, reporting, and data visualization skills, with an interest in manufacturing, logistics, and supply chain execution.

Requirements

Bachelor’s degree in: Supply Chain, Business Analytics, Data Analytics, Industrial Engineering, Operations Managementm, Statistics or related field
2–5+ years of experience in data analysis, supply chain analytics, or operational analytics
Experience working with manufacturing, logistics, or operational datasets
Strong proficiency in: Excel (advanced formulas, pivot tables, data analysis)
Data visualization tools (Power BI, Tableau, or similar)
Experience building and maintaining operational dashboards and recurring reports
Strong attention to detail and data quality

Nice To Haves

Experience with any of the following is a plus: Supply chain or manufacturing analytics
ERP, planning, or operational data systems
Basic statistical analysis or forecasting
Python or similar tools for data analysis
Familiarity with pharmaceutical, PET, or regulated manufacturing environments
Experience supporting operational, manufacturing, or supply chain teams
Experience analyzing production, logistics, or distribution network data
Ability to clearly communicate insights to technical and non-technical stakeholders
Demonstrated ability to work effectively within a data science / analytics team structure

Responsibilities

Develop, maintain, and enhance standard operational dashboards and reports under the guidance of the Supply Chain Data Scientist
Track key metrics including On-Time Delivery (OTD), reliability, utilization, and supply performance across manufacturing, quality, and logistics
Perform routine and ad-hoc analyses of manufacturing, QC, and distribution data to identify trends, variability, and performance drivers
Support real-time and historical reporting to improve operational visibility and responsiveness
Create and manage master data governance and support SQL database.
Support demand-to-capacity planning through descriptive and diagnostic analysis
Assist with data preparation and analysis for dose allocation and network balancing efforts
Contribute data and insights to initiatives focused on improving network efficiency and resilience
Perform analysis on large operational datasets to identify patterns, anomalies, and improvement opportunities
Apply basic statistical methods to monitor process performance and operational risk indicators
Support forecasting, planning, and optimization work led by the Supply Chain Data Scientist and Head of Materials Management through data preparation and analysis
Document assumptions, metrics definitions, and data logic to ensure clarity and repeatability
Work closely with and take analytical direction from the Supply Chain Data Scientist
Partner with: RadioPharma Operations leadership, Manufacturing and MSAT teams, External Manufacturing and CMO partners, Commercial demand planning teams, Digital / IT data teams
Translate operational questions into structured reports, analyses, and visualizations
Support continuous improvement of analytical tools, dashboards, and data models