Responsibilities

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of Applied AI/ML Scientists specialize in developing algorithmic solutions for recommender systems, spend optimization, lifetime value (LTV) predictions, and much more. Our ultimate goal is to empower local retail businesses with the tools they need to succeed.
At Faire, the Data 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 an Applied AI/ML Scientist tech lead, you will own vision, strategy, and execution for a set of problems that power Faire's two-sided marketplace, connecting hundreds of thousands of independent brands and retailers. Because our users are businesses, there is a wealth of information about them that ML models can use to personalize their experience, grow their success, and keep the marketplace high quality and trustworthy. You will work across structured and unstructured data using methods that range from personalization and recommendations to causal inference, lifetime-value modeling, information extraction, and LLMs. You will act as a lead across multiple cross-functional workstreams and mentor or manage other scientists on the team.
Our team already includes experienced Data Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for Applied AI/ML Scientists, and you will help take us there!
Drive data science vision, strategy, and execution end-to-end, and act as a pod lead across cross-functional workstreams.
Develop personalized recommendation, retrieval, and ranking models, and build content and retailer-level embeddings to power better discovery and exploration experiences.
Optimize marketing, acquisition, and incentive spend through targeting and personalization, and use experimentation and causal inference to measure the effectiveness of spend levers.
Predict lifetime value to prioritize sales and acquisition effort and to personalize the new-user experience, even in a user's first session.
Improve cold-start recommendations and exploration so that new users and new selection find the right audience quickly.
Extract insights from internal and external data (e.g. reviews, search behavior, referrals, and third-party sources) to enrich leads and power personalization.
Use deep learning, multi-modal LLMs, and human-in-the-loop training to understand listings and content, extract structured attributes, and detect issues with high accuracy.
Build detection, enforcement, and quality systems that reduce bad experiences in the marketplace (e.g. counterfeits, policy violations, poor service quality), using levers such as downranking and human-in-the-loop targeting.

Staff Applied AI/ML Scientist - Brand Growth & Marketplace Quality at Faire · LumiJobs

Staff Applied AI/ML Scientist - Brand Growth & Marketplace Quality

Responsibilities

Qualification

Required

Benefits

Company

Faire

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