Data Science Intern, Faire

• 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
• 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
• We value candidates who:
• Think deeply about modeling trade-offs and real-world constraints
• Care about both research rigor and practical impact
• Can independently drive ambiguous problems from idea to measurable outcome
• Are excited about applying cutting-edge ML to help small businesses thrive

Desirable

• 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
• 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
• Duration: 12–14 weeks (flexible start dates)
• Paid internship with potential for extension or return offer