{"id":"5110758030","title":"Data Science Intern - Personalization & Recommender Systems","posted_at":"2026-05-04T17:50:13.000Z","apply_url":"https://job-boards.greenhouse.io/faire/jobs/8512996002","locations":["San Francisco, CA"],"employment_type":"temporary","workplace_type":null,"seniority_level":"internship","description_language":"en","source_name":"greenhouse","source_url":"https://boards.greenhouse.io/faire/jobs/8512996002?gh_jid=8512996002","salary":{"min":75,"max":75,"currency":"USD","period":"hour","display":"$75/hour+"},"job_summary":"Faire is an online wholesale marketplace that uses machine learning to connect independent retailers with brands globally. This internship role focuses on developing and deploying algorithmic solutions for personalization and recommender systems to empower small businesses.","job_description":{"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 such as contextual bandits and reinforcement learning","Run large-scale A/B experiments to evaluate model performance in production","Contribute to the end-to-end ML lifecycle from problem formulation to online experimentation"],"minimum_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)","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, representation learning, sequential models, and reinforcement learning","Familiarity with offline evaluation metrics and online experimentation","Experience working with large-scale or production datasets"]},"visa_sponsorship":null,"experience_years_min":null,"job_address":null,"job_city":"San Francisco","job_state":"CA","job_country":"US","location_lat":37.7749295,"location_lng":-122.41941550000001,"keywords":["machine learning","data scientists","experimentation","recommendations","collaboration","user behavior","quantitative","Transformers","intelligence","large-scale","performance","competitive","engineering","end-to-end","enterprise","statistics","production","TensorFlow","engagement","trade-offs","extension","real-time","streaming","discovery","datasets","Platform","modeling","Flexible","evaluate","research","PyTorch","privacy","develop","design","Growth","global","deploy","Pandas","Python","Square","Google","local","craft","forms","data","SQL","art","ML","AI"],"company":{"name":"Faire","logo_url":"https://img.logo.dev/faire.com?token=pk_fWx5G5QrQMm-0Ud8BW3mBg&size=64&format=png","description":"Faire operates an online wholesale marketplace that uses data and machine learning to connect independent retailers with brands globally.","website_url":"https://faire.com","linkedin_url":"https://www.linkedin.com/company/fairewholesale","glassdoor_url":null,"x_url":"https://x.com/faire_wholesale","instagram_url":"https://www.instagram.com/faire_wholesale/","youtube_url":null,"github_url":null,"huggingface_url":null,"tiktok_url":"https://www.tiktok.com/@faire_wholesale","crunchbase_url":"https://www.crunchbase.com/organization/faire","facebook_url":"https://www.facebook.com/FaireWholesale","employee_count_range":"1001-5000","employee_count":null,"founded_year":2017,"headquarters":{"address":"100 Potrero Avenue, San Francisco, CA 94103, United States","city":"San Francisco, CA","country":"US","lat":37.7879363,"lng":-122.4075201},"industry":"ecommerce","company_type":"startup","total_funding_usd":1692000000,"locations":["Chicago, IL","Kitchener-Waterloo, Canada","Kitchener-Waterloo, ON","London, UK","London, United Kingdom","New York City, NY","New York, NY","San Francisco, CA","Toronto, Canada","Toronto, ON"]}}