Data Analyst
RX MEDICAL LLC · Oklahoma City - Oklahoma City, OK 73114 ·
- Category
- Data analytics
Onsite Data Analyst in Oklahoma City for Rx Medical, an independent sales agent for 40+ medical device manufacturers. Day to day: writing SQL and Python to extract, clean, validate, and analyze operational data, building datasets and reporting pipelines, applying statistics, and automating manual reporting to support business decisions.
Job Summary
Rx Medical is an independent sales agent for approximately 40+ medical device manufacturers, including Fortune 500 medical companies such as Zimmer Biomet, Orthofix and Breg.
We offer support devices and services for neurosurgical and orthopedic physicians. Healthcare providers we serve include surgeons, family practitioners, physician assistants, and hospital staff.
We are looking for a highly analytical, technically minded Data Analyst who enjoys working with numbers, solving complex problems, and using data to improve business decisions. The successful candidate will be comfortable working hands-on with data and code, translating ambiguous business questions into structured analyses, and communicating findings clearly to both technical and non-technical stakeholders. This role will support the full data lifecycle, including data acquisition, transformation, analysis, validation, reporting, and process improvement.
Responsibilities
- Analyze complex business and operational data to identify trends, explain performance, answer business questions, and provide actionable insights and reporting
- Write and maintain SQL and Python code to extract, clean, transform, validate, and analyze data from multiple sources
- Develop and maintain reliable datasets, data pipelines, queries, and analytical workflows; identify and resolve data quality issues
- Apply quantitative and statistical methods to identify, analyze, and interpret trends, patterns, anomalies, and relationships in complex datasets
- Partner with management and business stakeholders to translate business questions into clear analytical requirements and prioritize data needs
- Identify opportunities to automate manual processes, improve data quality, and make analytical and reporting workflows more efficient and scalable