Senior Applied AI/ML Scientist - Retailer Growth at Faire in San Francisco, CA

Job description

• 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 notification and recommender systems, advertising attribution, and LTV predictions. We are dedicated to building machine learning models that help our customers thrive.
• As a member of the Retailer Growth Data team focusing on the paid marketing and top-of-funnel acquisition channels, you will develop AI/ML systems that help activate new retailers and increase their engagement. There are a wide range of ML opportunities in paid marketing optimization, from bidding optimization, search keywords intelligence, smart audience targeting to incrementality and efficiency estimation. With AI fast-growing and changing every aspect of the world, AEO (Answer Engine Optimization) is the new chapter of growth that yet needs to be figured out, where there is a huge opportunity to leverage ML and LLM to create programmatic content at scale, build reinforcement learning systems for fast feedback loop, and optimize landing experience to improve conversion.
• Our team already includes experienced Applied AI/ML 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!

Responsibilities

• Drive data science vision, strategy, and execution within Retailer Growth, using AI/ML solutions to activate and engage more retailers on the platform
• Work with cross-functional stakeholders to develop end-to-end product solutions
• Extract deep behavioral insights using AI to automate AEO content creation and personalize the user landing experience.
• Optimize marketing capital allocation through sophisticated targeting and bidding optimization strategies.
• Implement rigorous experimentation and causal inference frameworks to quantify the impact of growth levers.
• Engineer scalable solutions for complex challenges inherent to two-sided marketplace dynamics.

Requirements

• 3+ years of industry experience using machine learning to solve real-world problems
• Experience with relevant business problems (e-commerce)
• Experience with relevant technical methods (LTV modeling, NLP, LLMs, causal ML, bidding optimization)
• Strong programming skills
• An excitement and willingness to learn new tools and techniques
• The ability to design and implement ML solutions without supervision
• Strong communication skills and the ability to work in a highly cross-functional team
• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
• Previous experience in paid marketing, and/or growth team focusing on SEO and AEO optimization
• Previous experience in LLMs and programmatic content generation

Skills

Required

• Strong programming skills

Benefits

• Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
• Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.
• Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.
• Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.
• Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.
• San Francisco: the pay range for this role is $211,000 to $290,500 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 and may be modified in the future.