Staff Data Scientist, Faire

• 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
• An excitement and willingness to learn new tools and techniques
• The ability to contribute to team strategy and to lead model development without supervision
• Strong communication skills and the ability to work in a highly cross-functional team

Desirable

• At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace
• We are dedicated to building machine learning models that help our customers thrive
• As the Marketplace Quality lead within the Brand Data Science team, you will be responsible for improving the quality of retailers’ experiences with products and brands on Faire, particularly by reducing egregious issues, such as counterfeit products, fulfillment delays, damaged and missing items, and poor quality products
• You will develop data science models to detect and act on issues
• This will involve issue detection from unstructured data, human-in-the-loop targeting, causal inference, and lever optimization
• You will independently drive data science vision, strategy, and execution and be the data science lead within the cross-functional Marketplace Quality pod, thinking end-to-end about the problem
• Our team already includes experienced Data Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists and machine learning engineers, and you will help take us there!
• Drive data science vision, strategy, and execution on Marketplace Quality, reducing the bad experiences that retailers encounter in the marketplace (e.g. counterfeits, late shipment, damaged and missing items, poor product quality)
• Act as a lead on the cross-functional Marketplace Quality pod, thinking end-to-end about brand and retailer experiences, ops processes, policy enforcement, etc
• Use deep learning techniques to match products across platforms, detect issues from image and text data, and summarize 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 Top Shop status 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