Senior Data Scientist / Machine Learning Engineer - Personalization - Remote

Overview

Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants.

By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.

In Short

• Work on cutting-edge personalization and recommendation problems.
• Use machine learning to help local retailers and independent brands succeed.
• Be a foundational team member of a fast-growing company.
• Collaborate with experienced Data Scientists and Machine Learning Engineers.
• Develop algorithmic solutions for various applications.
• Personalize recommendations across various platforms.
• Engage in a closely collaborative team environment.
• Explore diverse data sources for insights.
• Implement state-of-the-art algorithms.
• Contribute to the local retail community.

Requirements

• 4+ years of industry experience in machine learning.
• Strong understanding of personalization for product development.
• Proficient in Machine Learning and Deep Learning modeling.
• Willingness to learn new tools and techniques.
• Strong communication skills.

Benefits

• Competitive salary range of $192,000 to $264,000 per year.
• Equity and benefits eligibility.
• Opportunity to work in a hybrid schedule.
• Collaborative and innovative work environment.
• Impactful work supporting local businesses.