Data Scientist

Job Title: Data Scientist

About the Role:

As a Data Scientist in the Inventory & Merchandising team, you will be a key partner in driving data-driven insights to enhance the customer experience and improve inventory efficiency. You will collaborate closely with internal analytics stakeholders, as well as Machine Learning and customer experience product teams. Your analytical skills will be instrumental in ensuring data accuracy, developing insightful visualizations, conducting in-depth analysis, and ultimately contributing to strategic decision-making.

Responsibilities:

• Ensure Data Accuracy and Accessibility: Develop, maintain, and improve data rollups to guarantee data quality and provide reliable data sources for reporting and analysis.
• Develop Insightful Visualizations: Design and build interactive dashboards and reports in Looker to track key performance indicators (KPIs), identify trends, and communicate actionable insights to stakeholders.
• Provide Ad-Hoc Analysis and Reporting: Utilize Google BigQuery to efficiently query, analyze, and interpret large datasets to answer prioritized ad-hoc business questions and support strategic decision-making.
• Drive Data-Driven Decisions: Assist in analyzing A/B testing results to quantify the impact of proposed changes and ML model iterations.
• Collaborate and Communicate: Work closely with cross-functional teams, including Machine Learning Engineers and Customer Experience Specialists, to understand their needs and translate data insights into actionable strategies.

Must-Haves:

• Proven experience with Looker and other data visualization tools.
• Demonstrable experience developing data rollups, preferably on cloud data warehouses such as Big Query.
• Extensive experience using SQL for data querying and analysis.
• Proficiency in either R or Python for conducting statistical analysis and deep dives.

Nice-to-Haves:

• Experience working with clickstream data (visits, events, etc.).
• Experience with A/B experimentation methodologies and/or causal inference techniques.
• Experience working in a two-sided marketplace environment.
• Proficiency in Look ML and familiarity with Looker infrastructure (hub and spoke, etc.).
• Familiarity with basic statistical concepts (statistical significance, confidence intervals, etc.).