About the role
About Stitch Fix, Inc.
Stitch Fix (NASDAQ: SFIX) Stitch Fix 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. We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what’s possible for our clients, while we help you reach your full potential.
About the Role
Stitch Fix is redefining retail by blending the art of fashion with the science of machine learning. 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)—that are leveraged across multiple client experiences and downstream applications. We foster a culture of ownership where Data Scientists manage the full lifecycle of their work. As a Senior Full-Stack Data Scientist, you will 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 will tackle technically challenging problems in personalization and recommendation systems, working with large-scale behavioral, transactional, textual, and visual data.
This is a highly collaborative and hands-on role. You will partner with Data Scientists, ML Engineers, Product, and other technical teams to translate research and emerging ML techniques into reliable systems that create 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—including deep learning, representation learning, multimodal modeling, and generative approaches—where they can meaningfully improve our 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 and business impact, and communicate results to stakeholders and leadership.