Senior Applied AI/ML Scientist - Retailer Growth
About this role
The Retailer Growth Data team at Faire builds algorithmic solutions for notification and recommender systems, advertising attribution, and lifetime value predictions to help independent retailers compete against major wholesale platforms. Joining this group means developing end-to-end artificial intelligence and machine learning systems that drive retailer activation, engagement, and paid marketing optimization across top-of-funnel channels.
Daily responsibilities center on building scalable machine learning solutions for complex two-sided marketplace dynamics, extracting behavioral insights to automate answer engine optimization content creation, and personalizing user landing experiences. Practitioners will also allocate marketing capital through bidding optimization, smart audience targeting, and rigorous causal inference frameworks.
Ideal candidates are resourceful professionals with strong programming skills and industry experience solving real-world problems using machine learning. People who thrive here are comfortable designing independent solutions without supervision, communicating effectively with cross-functional partners, and adapting quickly to emerging artificial intelligence techniques.
What you will do
- Drive data science vision, strategy, and execution within the Retailer Growth group.
- Work alongside cross-functional stakeholders to develop end-to-end product solutions.
- Extract deep behavioral insights using artificial intelligence to automate answer engine optimization content creation and personalize user landing experiences.
- 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.
What you need
- Bring at least 3 years of industry experience using machine learning to solve real-world problems.
- Demonstrate familiarity with relevant business problems in e-commerce.
- Apply technical methods such as lifetime value modeling, natural language processing, large language models, causal machine learning, and bidding optimization.
- Possess strong programming skills and the ability to design and implement machine learning solutions without supervision.
- Maintain strong communication skills to collaborate effectively within a highly cross-functional team.
Nice to have
- Master's or PhD in Computer Science, Statistics, or related STEM fields
- Previous experience in paid marketing, growth teams focusing on SEO and AEO optimization
- Previous experience in LLMs and programmatic content generation
Benefits
- Competitive pay
- Equity
- Comprehensive benefits designed to support your life inside and outside of work
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
Employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing. Additionally, hybrid in-office roles have the flexibility to work remotely up to 4 weeks per year.
What is the salary range for this position?
The pay range for this role in San Francisco is $211,000 to $290,500 per year. This role is also eligible for equity and benefits.
What methods and technologies will I work with?
You will utilize machine learning to solve real-world e-commerce problems involving lifetime value modeling, natural language processing, large language models, causal machine learning, and bidding optimization.