Staff Data Scientist / Machine Learning Engineer - Ads

About the position

Faire is using machine learning to change wholesale and help local retailers compete with Amazon and big box stores. Our experienced data scientists and machine learning engineers are developing solutions related to discovery, ranking, search, recommendations, ads, logistics, underwriting, and more - all with the goal of helping local retail thrive. As a member of the Discovery Data team you'll be responsible for Ads / Sponsored Products: Joining a newly established team building Ads Delivery and Advertiser Optimization from the ground up and tackling challenges in ads targeting, retrieval, prediction/ranking, bidding, pacing, and auction design. Our team already includes experienced Data Scientists and Machine Learning Engineers from Uber, Airbnb, Square, Facebook, LinkedIn and Pinterest. We're a lean, talented team with high opportunity for direct product impact and ownership.

Responsibilities

• Join a newly established team building Ads Delivery and Advertiser Optimization from the ground up.
• Tackle challenges in ads targeting, retrieval, prediction/ranking, bidding, pacing, and auction design.

Requirements

• Experience with leading cross-team and cross-functional technical strategy and roadmap.
• 5+ years of industry experience using machine learning to solve real-world problems.
• Experience and strong understanding of search / personalization / ads for product development.
• Strong programming skills.
• An excitement and willingness to learn new tools and techniques.
• Experience with deep learning.
• The ability to contribute to team strategy and to lead model development without supervision.
• Strong communication skills and the ability to work with others in a closely collaborative team environment.

Nice-to-haves

• Master's or PhD in Computer Science, Statistics, or related STEM fields.
• Ability to quickly implement state of the art algorithms from an academic paper.

Benefits

• Equity and benefits eligibility.
• Flexible remote work up to 4 weeks per year.
• In-office work on a hybrid schedule (2 days per week).