Business Operations Research Analyst bei RxSense

• Job Title: Business Operations Research Analyst
• Employer: RxSense Holdings LLC
• Applicants are to send Resumes to: HR Manager Taneisha Hylton, thylton@rxsense.com
• Geographic Location of Job Opportunity: The employee can telecommute from any location within the U.S.
• Job Description/Requirements:
  • Research and analyze solutions to extend the company's cloud-based date warehouse;
  • Design and build relational and dimensional data models using ER modeling techniques that includes but not limited to data warehouses and transactional systems;
  • Collaborate with operations and business leaders to understand management objectives and apply them as basis for the utilization and analysis of data;
  • Retrieve and manipulate data, including data wrangling, data curation, gap analysis, and impact analysis, using SQL and Python;
  • Build statistical algorithms and data models to identify and analyze complex data patterns, solve business questions and predict future trends;
  • Work with development team to design informative data visualizations; communicate data insights to business stakeholders to identify problems and advise in making informed business decisions; and
  • Work with project management to prioritize data and reporting needs.

Employer requires a Master's degree in Business Analytics, Data Science, or a closely related field. Additionally, the successful candidate must have advanced knowledge of the following:

1. Relational database modeling with normal form schema designs, dimensional modeling techniques including but not limited to star and snowflake schema designs, data modeling and principles of ER modeling;
2. Descriptive statistical methods including but not limited to measures of dispersion, measures of position and inferential statistical methods including but not limited to predictive methods and hypothesis testing;
3. Creating and presenting data visualizations to show business performance indicators to evaluate opportunities and risks;
4. Data retrieval and manipulation including data profiling, data lineage, data assurance, data disposal, data imputation and gap analysis using SQL and Python.