Senior Manager, Recommendation Algorithms

Stitch Fix

4d•$185,000 - $245,000

About The Position

The Recommendation Algorithms team powers personalized shopping experiences across Stitch Fix, using machine learning and AI to help clients discover items and outfits they’ll love. The team turns model outputs, inventory, client context, and business objectives into relevant recommendations, rankings, search results, and personalized assortments across Fix and Freestyle. As the Senior Manager, Recommendation Algorithms, you will lead a team of Data Scientists and Machine Learning Engineers responsible for the strategy, development, and production performance of these client-facing personalization systems. You’ll partner closely with Product, Engineering, Merchandising, Enterprise Analytics, and other Algorithms teams to improve recommendation quality, evolve our technical approach, and deliver measurable client and business impact.

Requirements

Bachelor’s degree in Computer Science, Statistics, Mathematics, Physics, Operations Research, or another quantitative field; Master’s degree or PhD preferred.
5+ years of experience designing and deploying machine learning solutions, ideally within recommendation systems, search, ranking, personalization, e-commerce, or retail, with strong applied knowledge of areas such as candidate generation, scoring, ranking, retrieval, diversification, contextual personalization, search relevance, or multi-objective optimization.
2+ years of experience as a technical lead or people manager, with a demonstrated ability to develop strong technical talent, lead through others, and build a culture of ownership and technical excellence.
Strong technical foundation in production machine learning, including the ability to write and review production-grade code, ideally in Python, and experience with cloud infrastructure, distributed data systems, scalable ML architecture, experimentation, offline evaluation, and production monitoring.
Experience designing and interpreting A/B tests and evaluation frameworks, with the ability to connect model performance to client and business outcomes and communicate technical concepts, uncertainty, and tradeoffs to both technical and non-technical audiences.
Demonstrated ability to build trust and influence roadmaps across Product, Engineering, Analytics, Merchandising, and other cross-functional teams.
Working knowledge of generative AI, AI-assisted development tools, and emerging AI/ML approaches, with sound judgment around when these technologies add value alongside traditional machine learning and human expertise.

Responsibilities

Lead the strategy and execution of client-facing personalization across recommendations, ranking, search, discovery, outfits, and similar-item experiences, balancing relevance, diversity, freshness, inventory availability, client context, and business objectives.
Shape the roadmap with Product, Engineering, Design, Merchandising, Analytics, and Algorithms partners, making thoughtful tradeoffs across client experience, stylist needs, inventory objectives, recommendation quality, and system complexity.
Drive rigorous experimentation and measurement through offline evaluation and online A/B testing, using metrics such as engagement, selection, keep rate, items sold, revenue, margin, and client satisfaction to guide decisions and improve outcomes.
Own the end-to-end algorithm lifecycle, from opportunity sizing and modeling through experimentation, production deployment, monitoring, and operational support, while partnering with Engineering to build scalable, reliable, observable, and explainable recommendation systems.
Advance how the team uses AI and emerging technologies, including generative AI and LLM-based evaluation, to improve semantic understanding, recommendation and outfit quality, model evaluation, and team productivity while fostering a culture of technical excellence, ownership, learning, and collaboration.