Note: The job is a remote job and is open to candidates in USA. Stitch Fix is hiring a Principal Full-Stack Data Scientist for its Foundational Models team, which builds machine learning systems for personalization and recommendations. The role spans the full machine learning lifecycle, from developing and evaluating models to deploying and operating production systems, while collaborating across data science, engineering, and product teams.
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
- 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 understand downstream needs and translate them into reusable foundational ML capabilities
- Contribute to the technical direction of the Foundational Models team through design discussions, code reviews, research, prototyping, and development of engineering and modeling best practices
- Communicate complex technical concepts, modeling approaches, and tradeoffs, and recommendations clearly to both technical and non-technical partners
- Mentor and collaborate with other Data Scientists and engineers, helping raise the technical bar across the team
Skills
- Bachelor's Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required
- 8+ years of experience in design and deployment of machine learning solutions, ideally in personalization, such as recommendation systems, representation learning, or search
- Strong ability to architect technical solutions and write production-grade code in Python
- Ability to independently drive ambiguous machine learning problems from initial exploration and prototyping through production deployment, monitoring, iteration, and measurable impact
- Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Spark
- Experience with modern deep-learning frameworks such as PyTorch or TensorFlow
- Strong understanding of model evaluation and experimentation, including offline evaluation, A/B testing, and translating model improvements into measurable product or business outcomes
- Ability to reason about the tradeoffs involved in production ML systems, including model quality, latency, scalability, reliability, and computational cost
- Strong communication and collaboration skills, with the ability to influence technical decisions and communicate complex concepts effectively to both technical and non-technical stakeholders
- Intellectual curiosity and a demonstrated ability to learn and apply new machine learning techniques to practical problems
- Master's Degree or PhD
Benefits
- Annual bonus, depending on employee and company performance
- New hire and ongoing grants of restricted stock units, depending on employee and company performance
- Medical, dental, vision, and other benefits
Company Overview
Company H1B Sponsorship