Senior Applied AI/ML Scientist - Brand Growth
About this role
Helping independent retailers discover global products and compete with big box stores requires solving complex discovery and growth challenges on a two-sided wholesale platform. The Brand Growth team tackles this by building algorithmic solutions that connect brands and retailers through personalization, recommendations, advertising attribution, and lifetime-value prediction.
As a Senior Applied AI/ML Scientist on this team, the hire will own modeling and measurement for brand acquisition and growth initiatives. Day-to-day work involves driving projects end-to-end across structured and unstructured data, utilizing methods like causal inference and information extraction, and partnering closely with product, engineering, marketing, and sales.
Someone who thrives in this environment has at least three years of industry machine learning experience, enjoys navigating marketplace dynamics, and can design implementations independently. The team brings together experienced scientists from across the tech industry, offering an opportunity to shape how machine learning scales marketplace success.
What you will do
- Manage applied machine learning projects from problem framing and model building to deployment and impact measurement.
- Optimize marketing spend and brand acquisition strategies through targeted lookalike audiences and personalization techniques.
- Forecast brand lifetime value to prioritize sales leads and tailor onboarding experiences for new marketplace participants.
- Enhance cold-start recommendations and exploration mechanics so newly joined brands quickly find relevant retailer audiences.
- Enrich brand leads using internal and external data sources such as search behavior and third-party references.
- Apply experimentation and causal inference methodologies to evaluate the performance of growth and spend levers.
- Collaborate across product, engineering, marketing, sales, and analytics teams to translate models into shipped products.
What you need
- Minimum of three years of professional industry experience applying machine learning to real-world problems.
- Familiarity with e-commerce environments and related business use cases.
- Hands-on technical background in lifetime-value modeling, natural language processing, large language models, causal machine learning, or bidding optimization.
- Demonstrated programming proficiency and strong overall software skills.
- Capacity to design and execute machine learning solutions with minimal supervision.
- Effective communication skills combined with a history of working in cross-functional teams.
Nice to have
- Master's or PhD in Computer Science, Statistics, or related STEM fields
- Previous experience in marketplace growth, acquisition or paid marketing, LTV modeling, or personalization for a two-sided platform
Benefits
- Equity
- Comprehensive benefits designed to support life inside and outside of work
Skills and tech stack
Questions candidates ask
What is the remote work and office policy for this role?
Hybrid 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, and the role is also eligible for equity and benefits.
What types of methods and problems will I work on?
You will work across structured and unstructured data using methods that range from personalization and recommendations to causal inference, lifetime-value modeling, and information extraction. Projects focus on helping Faire acquire and grow the brands on the marketplace.
How does the company screen applicants?
Faire uses Artificial Intelligence to screen and select applicants for this position.