About this role

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!

What you'll do

• 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.
• Re-engage users through personalized marketing and identify gaps and opportunities that drive increased engagement.
• Mentor or manage Senior Applied AI/ML Scientists and Analytics Engineers.
• Solve challenging problems related to a two-sided marketplace.

Qualifications

• 5+ years of industry experience using machine learning to solve real-world problems.
• Experience with relevant business problems (e-commerce, marketplaces, growth, personalization, incentives, or trust and quality).
• Experience with relevant technical methods (causal inference, predictive/LTV modeling, recommendations, information extraction, entity resolution, and/or deep learning and LLMs).
• Strong programming skills.
• An excitement and willingness to learn new tools and techniques.
• Experience as a tech lead, mentoring other scientists or machine learning engineers, and the ability to set team strategy and lead model development without supervision.
• Strong communication skills and the ability to work in a highly cross-functional team.

Great to Haves:

• Highly recommended: Master's or PhD in Computer Science, Statistics, or related STEM fields.
• Experience as a tech lead manager or people manager of applied ML / data science teams.
• Previous experience in marketplace growth, incentive optimization, or trust and quality for a two-sided platform.
• Previous experience with deep learning or language models.

Salary Range

San Francisco: the pay range for this role is $246,500 to $339,000 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change.

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.