Data Analyst - Full Remote - Fashion/e-commerce

1.) About the company

A e-commerce fashion company, is looking to expand its cross-platform product in Japan and overseas. They are looking for a data analyst to join their team, to identify issues and plan initiatives based on behavioural data. They recently had over 8 million monthly customers, and are continuing to grow and expand the company.

You would leverage data to assist  multiple departments including product development, management planning, marketing, logistics, and customer support, to solve challenges and drive the business forward.

The demand for data analysts is growing rapidly in this industry, and particularly with this company, you would have the opportunity to work across multiple departments, expanding your role and responsibilities, making it a strong career choice.

2.)  Who Are you

• You are an experienced Data Analyst.
• You are a self-motivated individual that enjoys working in a team.
• You love working with different data and 
• You are Fluent in Japanese

3.)  Why you should apply

• Global and domestic product - fashion / sneaker goods.
• Talented team coming from Mercari, Samsung, Apple, BizReach, MIXI etc
• Culture - Most employees are in their late 20's & 30's with a flat management system.
• Flexible work arrangements - full remote work, or hybrid, your choice!
• Salary revisions every 6 months, with lots of flexibility to grow into senior roles.

4.) Requirements

• More than 3 years of practical experience in data analysis
• Experience in data aggregation and processing using SQL
• Basic understanding of statistics (approximately level 2 in statistics)
• Experience in service improvement in collaboration with product managers, designers, and engineers

5.) Nice to have

• Experience in data analysis using user data from large-scale services
• Experience in data analysis using Python or R
• Experience in data analysis for marketplaces
• Experience in analyzing the effectiveness of online and offline advertising
• Experience in effectiveness verification using causal inference methods
• Experience in user research