Senior Applied AI/ML Scientist - Retailer Growth

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)

The ability to design and implement ML solutions without supervision

Previous experience in paid marketing, and/or growth team focusing on SEO and AEO optimization

Previous experience in LLMs and programmatic content generation

Canada: the pay range for this role is $180,000 to $247,500 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.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

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.

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.