Data Science Intern

About Faire

Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. 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 businesses can grow and local communities can thrive.

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.

Data Science Internship — Multiple Teams

Faire leverages machine learning and data insights to transform the wholesale industry, giving independent retailers the tools to compete with large-scale e-commerce platforms and big-box stores. Our Data Science team builds and maintains the algorithmic systems — spanning search, personalization, recommendation, and ranking — that power our marketplace and help our customers thrive.

We are hiring Data Science interns across several teams and are looking for intellectually curious, self-directed problem solvers eager to work end-to-end on high-impact challenges, from data exploration to production-ready solutions.

Our internships are paid, 12–14 weeks in duration, with flexible start dates. Extensions are considered based on project scope and mutual interest.

Open Teams

Search & Recommendation

• Design and deploy state-of-the-art recommender systems that power ranking and discovery across the marketplace
• Develop rich user and item representations through embeddings, sequence models, and graph-based methods
• Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at scale
• Apply exploration–exploitation strategies — including contextual bandits and reinforcement learning — to optimize recommendations under uncertainty
• Advance recommendation quality through improvements to diversification, novelty, and long-term user engagement
• Own the full ML lifecycle: from problem formulation and modeling through offline evaluation and online experimentation

Risk Management

• Build and refine models and heuristics across core risk domains — including underwriting, identity verification, returns, markdowns, and disputes & misuse — to reduce financial losses and unlock GMV growth
• Partner cross-functionally to develop scalable, data-driven frameworks that balance risk exposure with business opportunity

What You'll Do

• Design, develop, and A/B test cutting-edge machine learning algorithms and analytical solutions, with guidance from senior