Staff Applied AI/ML Scientist, Acquisition

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 the tech lead for Acquisition, you will own data science vision, strategy, and execution for how Faire finds and acquires the independent retailers who shop on our marketplace. You will build the models that decide where and how much we spend across paid channels, predict the long-term value of prospective retailers, generate the landing pages and content that bring them to Faire, and shape the experience retailers have before they sign up. You will lead a small team of scientists and analytics engineers and act as a pod lead alongside product, engineering, and marketing partners.

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 AI/ML vision, strategy, and roadmap for Acquisition and Paid Marketing, and act as a pod lead across cross-functional workstreams.
• Optimize paid marketing spend, bidding, and budget allocation across channels such as search, social, and Performance Max.
• Predict retailer lifetime value and use it to power value-based bidding and to prioritize acquisition effort.
• Design experiments to measure the incremental impact of paid channels and spend levers, and turn the results into investment decisions.
• Develop keyword, SEO, and AEO intelligence that identifies and prioritizes the search and content opportunities most likely to bring high-value retailers to Faire.
• Generate landing pages and content at scale, and measure their impact on acquisition.
• Build audience and targeting models for retargeting campaigns.
• Manage and mentorApplied AI/ML Scientists and Analytics Engineers, and grow the team through hiring.
• Partner closely with marketing, product, engineering, and finance to translate models into measurable growth.

Qualifications

• 5+ years of industry experience using machine learning to solve real-world problems.
• Experience with relevant business problems (e-commerce, marketplaces, growth, acquisition, paid marketing, or personalization).
• Experience with relevant technical methods (incrementality measurement, LTV modeling, bidding and budget optimization, recommendations and cold-start, and/or LLMs for production solutions).
• 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, Economics, or related STEM fields.
• Experience as a tech lead manager or people manager of applied ML / data science teams.
• Previous experience in performance / paid marketing, marketing-mix or media-mix modeling, bidding systems, incentive optimization, or growth 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.