Ecommerce Data Analyst for Product Pricing and Listing

Our growing ecommerce company is looking for a detail-oriented, data-driven individual with a strong background in numbers to join our team as an Ecommerce Product Pricer and Lister. The ideal candidate will have experience in pricing, an education in data science, engineering, accounting, or finance, and a solid understanding of financial analysis.

Responsibilities:
Reprice, list, and improve product listings on a daily basis for various sales channels, including Amazon, Walmart, eBay, and Shopify.
Utilize data and various files to optimize the repricing process.
Work with excel to manage and manipulate large data ---------- e repricing software such as BQool and Seller Champ, and tools like Helium 10 and Keepa to make informed pricing decisions.
Collaborate with the management team and the accountant to achieve the company's goals.
Requirements:
Prior experience in pricing and an educational background in data science, engineering, accounting, or finance.
Strong proficiency in Excel and experience working with large data sets.
Familiarity with ecommerce platforms such as Amazon, Walmart, eBay, and Shopify.
Knowledge of repricing software (BQool and Seller Champ) and tools like Helium 10 and Keepa is a plus.

KPIs:
Increased sales on various ecommerce platforms.
Improved efficiency and accuracy in repricing and listing processes.
Effective use of data and tools to make informed pricing decisions.

Additional Information:

This is a full-time position with a flexible schedule, requiring at least 2 hours of overlap with 9-5pm EST.
The role offers opportunities for growth, including distribution pricing support and purchasing support.
Our management style involves hands-on support, frequent check-ins, and 10 hours of recorded training for the candidate to review as needed.

How to Apply:
Respond to this post with your resume and additional documentation you may have. Also answer the question below.

Scenario: You are provided with a dataset containing sales data for an ecommerce store over the past year. The dataset includes information such as product category, units sold, revenue, and the date of each sale.

Question: Describe the steps you would take to analyze this dataset and identify potential areas for improvement in the store's sales performance. Consider which metrics you would focus on, any tools or techniques you would use, and how you would present your findings to the management team.