SESAC is looking for a Data Analyst to join our Royalty Distribution and Research Services (RDRS) team. We are open to local Nashville applicants as well as remote work from home candidates. The Data Analyst will support SESAC’s RDRS Department by gathering metrics/data, analyzing relevant information, and providing timely reports and recommended actions based on the analysis.
What You Will Be Doing:
- Collecting, organizing and assembling data from disparate sources (both internal and external), and prepare resulting data for analysis.
- Performing data analysis, including summarization, exploratory analysis, trend analysis, and other techniques as necessary.
- Collaborating with others to interpret data and develop recommendations based on findings.
- Working with engineering team to assist in designing and creating the necessary frameworks to support organizational analytical needs.
- Providing supporting research for special projects, including ownership of song catalogs, related industry developments and trends, and other information as needed.
- Preparing ad-hoc reports as requested.
What Make Your Qualified:
- Bachelor’s degree in Business, Finance, Economics, Statistics or similar discipline.
- 1+ years of experience in an analyst role.
- Expert user of Microsoft Office suite (especially advanced Excel skills).
- Experience with SQL querying relational databases (especially Oracle), familiarity with Amazon Web Services data suite is a plus but not required.
- Familiarity with data exploration / data visualization tools like Tableau, Chartio, Amazon QuickSight, etc.
- Proven track record of strong analytical and decision-making skills.
- Well-developed communication (oral and written) and interpersonal skills with the ability to convey complex information and ideas in an easy-to-understand manner.
- Strong organizational skills, including the ability to prioritize and multi-task in a fast-paced environment.
- Experience with programming languages (e.g. R, Python, etc.), Machine learning concepts (supervised learning models), Nielsen data, and copyright law are a preferred but not required.