Data Science Intern (Personalization & Recommender Systems)

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 (c