Remote opportunity at
Stitch FixPrincipal Full-Stack Data Scientist - Foundational Models
Stitch Fix operates as an online retail platform that integrates human fashion styling with advanced machine learning and generative artificial intelligence. The organization seeks a Principal…
Career Tools
About This Role
Stitch Fix operates as an online retail platform that integrates human fashion styling with advanced machine learning and generative artificial intelligence. The organization seeks a Principal Full-Stack Data Scientist to join its Foundational Models team on a remote basis. In this role, the professional will build and iterate upon core recommendation engines, client and item representations, retrieval systems, and foundational architectures like the Client Time Series Model. This position is…
Job Description
Stitch Fix operates as an online retail platform that integrates human fashion styling with advanced machine learning and generative artificial intelligence. The organization seeks a Principal Full-Stack Data Scientist to join its Foundational Models team on a remote basis. In this role, the professional will build and iterate upon core recommendation engines, client and item representations, retrieval systems, and foundational architectures like the Client Time Series Model.
This position is designed for practitioners who prefer managing machine learning workflows from end to end. The selected candidate will formulate problems, explore data, prototype solutions, deploy models into production, and monitor performance over time. Daily duties involve tackling intricate personalization challenges by analyzing massive volumes of behavioral, transactional, textual, and visual data.
Successful applicants will partner closely with product managers, machine learning engineers, and fellow data scientists to convert emerging methodologies into reliable production systems. Individuals joining the group will contribute directly to the technical direction of foundational modeling through code reviews, design discussions, research, and mentoring teammates.
Responsibilities
- Design, build, evaluate, and productionize machine learning algorithms to enhance recommendation and personalization systems
- Advance foundational modeling elements including embeddings, client and item representations, ranking, retrieval, and assortment generation
- Explore and integrate large language models, deep learning, multimodal modeling, and generative approaches
- Manage the entire machine learning lifecycle from initial data exploration to deployment, monitoring, and ongoing iteration
- Create offline evaluations and online A/B tests to gauge model performance and business impact
- Process massive behavioral and product datasets utilizing Python, SQL, and distributed frameworks
- Construct production-ready solutions focusing on scalability, low latency, reliability, cost efficiency, and observability
- Partner with cross-functional teams including Product and Engineering to deliver reusable foundational capabilities
- Contribute to team strategy via research, prototyping, architectural discussions, and setting engineering best practices
- Communicate complex technical concepts and model tradeoffs clearly to technical and non-technical audiences
- Mentor and collaborate with other engineers and data scientists
Requirements
- At least 8 years of professional background designing and deploying machine learning applications, specifically in search, recommendation systems, or representation learning
- Advanced capability to architect technical solutions and write production-grade code in Python
- Track record of independently guiding ambiguous machine learning challenges from prototyping through production deployment and monitoring
- Hands-on experience processing large datasets using SQL and distributed processing technologies like Spark
- Familiarity with contemporary deep learning frameworks such as TensorFlow or PyTorch
- Strong grasp of experimentation, model evaluation, and translating technical upgrades into concrete business results
- Ability to balance tradeoffs between model quality, computational cost, latency, and system scalability
- Effective communication skills to influence technical decisions across diverse stakeholder groups
Qualifications
- Bachelor degree in a quantitative discipline such as Mathematics, Physics, Statistics, Computer Science, or a closely related field
- Master degree or PhD in a quantitative subject
Core Skills
Benefits
- Competitive salary range between $200,000 and $237,000 USD for US-based hires
- Eligibility for an annual bonus program
- New hire and ongoing grants of restricted stock units
- Comprehensive medical, dental, and vision health coverage
- Inclusive health and wellness benefits
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this position remote?
Yes, this is a remote employment opportunity.
What is the salary range for the role?
For US-based employees, the salary range is between $200,000 and $237,000 USD, determined by factors such as location, experience, and background.
What educational background is required?
A Bachelor degree in a quantitative field like Computer Science, Mathematics, Physics, or Statistics is required, while a Master degree or PhD is preferred.
What additional compensation is provided beyond the base salary?
The role is eligible for an annual performance bonus along with new hire and ongoing grants of restricted stock units.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.