Data Science Intern (Personalization & Recommender… at Faire · San…

Summary: Faire is an online wholesale marketplace that empowers independent retailers through technology and data insights. The Data Science Intern will focus on developing algorithmic solutions for personalization and recommender systems, working closely with machine learning engineers to impact marketplace outcomes. Responsibilities: - 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 Required 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 - Large-scale retrieval and ranking systems - Familiarity with offline evaluation metrics (NDCG, MAP, recall) and online experimentation - Experience working with large-scale or production datasets Required Skills: Recommender Systems, Personalization, Machine Learning, Ranking, Representation Learning, Python, PyTorch, TensorFlow, Pandas, SQL, Statistics, Sequential Models, Reinforcement Learning, Offline Evaluation Metrics, Online Experimentation