Data Science Intern (Personalization & Recommender Systems)
About The Position
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.
Requirements
- 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
- Demonstrated strong interest in recommender systems / personalization
- Experience with modern ML approaches to ranking and representation learning
Nice To Haves
- 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
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
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What This Job Offers
Career Level
Intern
Number of Employees
501-1,000 employees
