Faire is looking to improve the quality of retailers' experiences with products and brands on their marketplace by reducing egregious issues such as counterfeit products, fulfillment delays, damaged and missing items, and poor quality products.
Requirements
- 5+ years of industry experience using machine learning to solve real-world problems
- Experience with relevant business problems (e-commerce, marketplaces, or logistics)
- Experience with relevant technical methods (human-in-the-loop machine learning, causal inference, and/or deep learning)
- Strong programming skills
- Experience with LLMs
- Experience with causal inference methods
- Experience with deep learning or language models (preferred)
Responsibilities
- Drive data science vision, strategy, and execution on Marketplace Quality
- Act as a lead on the cross-functional Marketplace Quality pod
- Use LLMs to extract structured information from reviews
- Use causal inference methods to estimate the long term effects of quality issues and optimize the tradeoff between reducing issues and near-term growth
- Optimize levers such as downranking and metrics shown to retailers to drive visibility away from low quality and toward high quality products and brands
- Develop human-in-the-loop machine learning systems for detecting issues and targeting actions
- Solve challenging problems related to a two-sided marketplace
Other
- Master's or PhD in Computer Science, Statistics, or related STEM fields (highly recommended)
- Previous experience in marketplace quality in a two-sided e-commerce platform (preferred)
- Strong communication skills and the ability to work in a highly cross-functional team
- The ability to contribute to team strategy and to lead model development without supervision
- Excitement and willingness to learn new tools and techniques
