Data Science Intern at Faire

San Francisco, CAInternshipAlgorithms & DataPosted 21 days ago

About the Role

<div class="content-intro">About Faire Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive. 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.</div>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 <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Design and deploy state-of-the-art recommender systems that power ranking and discovery across the marketplace</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Develop rich user and item representations through embeddings, sequence models, and graph-based methods</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at scale</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Apply exploration–exploitation strategies — including contextual bandits and reinforcement learning — to optimize recommendations under uncertainty</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Advance recommendation quality through improvements to diversification, novelty, and long-term user engagement</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Own the full ML lifecycle: from problem formulation and modeling through offline evaluation and online experimentation</li> </ul> Fulfillment <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Develop ML models that predict product demand for brands leveraging Faire's fulfillment services, informing replenishment decisions, reducing stockouts, and improving inventory reliability across the platform</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Apply machine learning and optimization techniques to enhance the efficiency of Faire's fulfillment operations, including inventory placement, order packing logic, and operational workflow improvements</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Collaborate with the Discovery team and internal stakeholders to optimize surfaces that promote fulfillment products to retailers, driving discoverability and supporting the growth of Faire's fulfillment offering</li> </ul> Risk Management <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">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</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Partner cross-functionally to develop scalable, data-driven frameworks that balance risk exposure with business opportunity</li> </ul> What You'll Do <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2">Design, develop, and A/B test cutting-edge machine learning algorithms and analytical solutions, with guidance from senior technical leads</li> <li class="whitespace-normal break-words pl-2">Communicate project objectives, methodologies, and results clearly to both immediate teammates and broader cross-functional stakeholders</li> <li class="whitespace-normal break-words pl-2">Navigate the complexity of a two-sided marketplace, identifying and addressing the unique challenges that arise at the intersection of retailer and brand needs</li> </ul> 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 <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Publications or submissions to top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, or SIGIR</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">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</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Familiarity with offline evaluation metrics (NDCG, MAP, recall) and online experimentation</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Experience working with large-scale or production datasets</li> </ul> Fulfillment <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Experience with Python; familiarity with Java, Kotlin, or C++ is a plus</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Production-level experience building and deploying ML systems</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Working knowledge of statistical methods, including experimentation and causal inference</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Experience with SQL or other query languages preferred</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Genuine enthusiasm for tackling ambiguous problems and learning new tools and techniques</li> </ul> Risk Management <ul class="[li_&]:mb-0 [li_&]:mt-1 [li_&]:gap-1 [&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3"> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Solid ML fundamentals with hands-on experience productionizing models using frameworks such as scikit-learn, XGBoost, or deep learning libraries</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Experience with Python; familiarity with Java, Kotlin, or C++ is a plus</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Knowledge of statistical techniques including experimentation and causal inference</li> <li class="whitespace-normal break-words pl-2" style="font-style: italic;">Experience with SQL or other database querying languages preferred</li> </ul> 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. #LI-DNI<div class="content-conclusion">Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.  Why you’ll love working at Faire <ul> <li>Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.</li> <li>Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.</li> <li>Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.</li> <li>Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.</li> <li>Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.</li> </ul> Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our <a href="https://blog.faire.com/">blog</a>. Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs.  To request reasonable accommodation, please fill out our <a href="https://docs.google.com/forms/d/e/1FAIpQLSczpI9me7nYKhunSC6AlagHKv75LHqfi-Qi2TP_3dpin0Uc9Q/viewform">Accommodation Request Form</a> (<a href="https://bit.ly/faire-form)">https://bit.ly/faire-form)</a> Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s <a class="c-link" href="https://www.faire.com/privacy" target="_blank" data-stringify-link="https://www.faire.com/privacy" data-sk="tooltip_parent">Privacy Notice (https://www.faire.com/privacy)</a></div>

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