Data Science Intern (Personalization & Recommender Systems)

About Faire 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. About this role: 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 data scientists and machine learning engineers specialize in developing algorithmic solutions for search, personalization, recommender systems, and ranking. Our ultimate goal is to empower local retail businesses with the tools they need to succeed. We are looking for exceptional Master’s and PhD candidates specializing in recommender systems, personalization, or applied machine learning. This role is ideal for candidates who have: Demonstrated strong interest in recommender systems / personalization Experience with modern ML approaches to ranking and representation learning For PhD candidates: a track record of publications or submissions to top-tier venues (e.g., KDD, RecSys, ICML, NeurIPS, WWW, SIGIR) For Master’s candidates: high-impact research projects, internships, or open-source work in relevant areas You will work on core personalization problems that directly affect millions of recommendations per day, partnering closely with ML engineers to bring research ideas into production. What You’ll Work On Design and deploy state-of-the-art recommender systems for ranking and discovery Develop user and item representations using embeddings, sequence models, or graph-based methods Build systems leveraging real-time and streaming signals for dynamic personalization Apply exploration–exploitation techniques (e.g., contextual bandits, reinforcement learning) Improve diversification, novelty, and long-term user engagement Run large-scale A/B experiments to evaluate model performance in production Contribute to the end-to-end ML lifecycle: problem formulation → modeling → offline evaluation → online experimentation Why This Role is Unique Research → Production impact: Your work will directly ship and affect marketplace outcomes Rich, real-world data: Sparse, noisy, and high-dimensional data at scale Challenging objectives: Multi-sided marketplace optimization (retailers, brands, platform) Strong ML culture: Close collaboration with experienced ML engineers and applied scientists Opportunity to publish and build on prior research, where applicable Basic Qualifications Currently pursuing or recently completed a Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field Proficiency in Python and familiarity with the modern ML stack (e.g., PyTorch, TensorFlow, Pandas, SQL) A solid theoretical foundation in machine learning and statistics Preferred Qualifications Publications or submissions in top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, SIGIR Experience with: Recommender systems (collaborative filtering, deep recommenders, ranking) Representation learning / embeddings Sequential models (RNNs, Transformers for user behavior) Bandits / reinforcement learning L ... (truncated, view full listing at source)