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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Science interns - **Company:** Hybrid Faire - **Location:** San Francisco, CA, United States (Remote available) - **Salary:** $156,000.0 - **Contract:** Internship / Graduate position - **Skills:** Java (Programming Language), Data Analysis, C++ (Programming Language), Database Queries, Python (Programming Language), Recommender Systems, SQL Databases, Reinforcement Learning, Deep Learning, Kotlin, Scikit Learn, Information Technology, Data Analytics, Xgboost, Machine Learning Operations, Multiaccess Edge Computing - **Published:** June 9, 2026 - **Apply:** https://www.dice.com/job-detail/22d5e4e7-fa31-4f65-8b70-911d4e92d38c ## About the Role We're looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours., 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., All candidates must be currently enrolled or entry level 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, * 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 ## Description * 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 ## Related Videos - [Kotlin Multiplatform - True power of native code reuse](https://www.wearedevelopers.com/videos/4-kotlin-multiplatform-true-power-of-native-code-reuse) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) ## Related Articles - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [Best Coding Boot Camps in Germany](https://www.wearedevelopers.com/magazine/237-best-coding-boot-camps-in-germany) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)