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About This 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.
- 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.
About You
This is what you'll need to succeed in this role from day 1.
Requirements:
- Bachelor's Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required. Master's Degree or PhD preferred.
- 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.
Why You'll Love Working at Stitch Fix
- We are a group of bright, kind people who are motivated by challenge. We value integrity, innovation and trust. You'll bring these characteristics to life in everything you do at Stitch Fix.
- We cultivate a community of diverse perspectives—all voices are heard and valued.
- We are an innovative company and leverage our strengths in fashion and tech to disrupt the future of retail.
- We win as a team, commit to our work, and celebrate grit together because we value strong relationships.
- We boldly create the future while keeping equity and sustainability at the center of all that we do.
- We are the owners of our work and are energized by solving problems through a growth mindset lens. We think broadly and creatively through every situation to create meaningful impact.
- We offer comprehensive compensation packages and inclusive health and wellness benefits.
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Salary Context
This $200K-$237K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Stitch Fix, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($218K) sits 15% above the category median. Disclosed range: $200K to $237K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Stitch Fix AI Hiring
Stitch Fix has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US. Compensation range: $237K - $237K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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