Data Science Internship Jobs in San Francisco, CA

Data Science Internship - Multiple Teams

Faire leverages machine learning and data insights to transform the wholesale industry, giving independent retailers the tools to compete with large-scale e-commerce platforms and big-box stores. Our Data Science team builds and maintains the algorithmic systems - spanning search, personalization, recommendation, and ranking - that power our marketplace and help our customers thrive.

We are hiring Data Science interns across several teams and are looking for intellectually curious, self-directed problem solvers eager to work end-to-end on high-impact challenges, from data exploration to production-ready solutions.

Our internships are paid, 12-14 weeks in duration, with flexible start dates. Extensions are considered based on project scope and mutual interest.

Open Teams

Search & Recommendation

• Design and deploy state-of-the-art recommender systems that power ranking and discovery across the marketplace
• Develop rich user and item representations through embeddings, sequence models, and graph-based methods
• Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at scale
• Apply exploration-exploitation strategies - including contextual bandits and reinforcement learning - to optimize recommendations under uncertainty
• Advance recommendation quality through improvements to diversification, novelty, and long-term user engagement
• Own the full ML lifecycle: from problem formulation and modeling through offline evaluation and online experimentation

Risk Management

• Build and refine models and heuristics across core risk domains - including underwriting, identity verification, returns, markdowns, and disputes & misuse - to reduce financial losses and unlock GMV growth
• Partner cross-functionally to develop scalable, data-driven frameworks that balance risk exposure with business opportunity

What You'll Do

• Design, develop, and A/B test cutting-edge machine learning algorithms and analytical solutions, with guidance from senior technical leads
• Communicate project objectives, methodologies, and results clearly to both immediate teammates and broader cross-functional stakeholders
• Navigate the complexity of a two-sided marketplace, identifying and addressing the unique challenges that arise at the intersection of retailer and brand needs

What We're Looking For

All candidates must be currently enrolled or recently graduated Master's or PhD students in Computer Science, Operations Research, Statistics, Econometrics, or a related technical discipline. Beyond that, we're looking for team-specific experience:

Search & Recommendation Systems

• Publications or submissions to top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, or SIGIR
• Experience with recommender systems (collaborative filtering, deep recommenders, ranking), representation learning and embeddings, sequential models (RNNs, Transformers for user behavior modeling), bandit and reinforcement learning methods, and 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

Risk Management

• Solid ML fundamentals with hands-on experience productionizing models using frameworks such as scikit-learn, XGBoost, or deep learning libraries
• Experience with Python; familiarity with Java, Kotlin, or C++ is a plus
• Knowledge of statistical techniques including experimentation and causal inference
• Experience with SQL or other database querying languages preferred

Pay rate:

San Francisco: the pay rate for this role is $75 USD per hour.

Actual hourly pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

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