Principal Full-Stack Data Scientist - Foundational Models
Stitch Fix · Remote, USA
About this role
**About Stitch Fix, Inc.** Stitch Fix (NASDAQ: SFIX) is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we’re equally committed to building yours. **About the Role** The Foundational Models team builds and evolves the machine learning systems that power personalization and recommendations across Stitch Fix. The team owns foundational capabilities—including client and item representations, recommendation and retrieval systems, and core models such as the Client Time Series Model (CTSM)—leveraged across multiple client experiences and downstream applications. As a **Senior Full-Stack Data Scientist**, you’ll work across the full machine learning lifecycle: identifying opportunities, developing and evaluating new modeling approaches, running large-scale experiments, and deploying and operating models in production. You’ll tackle technically challenging problems in personalization and recommendation systems using large-scale behavioral, transactional, textual, and visual data. This is a highly collaborative, hands-on role partnering with Data Scientists, ML Engineers, Product, and other technical teams to translate research and emerging ML techniques into reliable systems with measurable client and business impact. **Responsibilities** - Design, develop, evaluate, and productionize machine learning models that improve Stitch Fix’s personalization and recommendation systems. - Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation. - Explore and apply LLMs and machine learning techniques (deep learning, representation learning, multimodal modeling, and generative approaches) where they can meaningfully improve systems. - Own work across the full ML lifecycle—from problem formulation and data exploration through modeling, experimentation, deployment, monitoring, and iteration. - Design rigorous offline evaluations and online experiments to understand model performance, measure client/business impact, and communicate results to stakeholders and leadership. - Work with large-scale behavioral and product datasets using Python, SQL, and distributed data-processing tools. - Build production-quality ML solutions with attention to scalability, reliability, latency, observability, and cost. - Collaborate with Product, Engineering, and other Data Science teams to translate downstream needs into reusable foundational ML capabilities. - Contribute to the technical direction of the Foundational Models team through design discussions, code reviews, research, prototyping, and best practices. - Communicate complex technical concepts, modeling approaches, and tradeoffs clearly to technical and non-technical partners. - Mentor and collaborate with other Data Scientists and engineers to raise the technical bar across the team. **About You** **Requirements** - Bachelor’s degree in a quantitative field (Computer Science, Statistics, Physics, Mathematics, or related); Master’s or PhD preferred. - 8+ years of experience designing and deploying machine learning solutions, ideally in personalization (recommendation systems, representation learning, or search). - Strong ability to architect technical solutions and write production-grade Python. - Ability to independently drive ambiguous ML problems from exploration and prototyping through production dep
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