Senior Manager, Growth Algorithms

Stitch Fix

8h•$200,000 - $245,000

About The Position

At Stitch Fix, we are at the forefront of innovation, creating cutting-edge solutions that blend fashion, technology, and data science. Our data science team combines machine learning with expert human judgment to generate innovative recommendations and insights that transform the way our clients discover what they love. We believe in a curiosity-driven data science culture where members are empowered to deliver impact through end-to-end model development. The diversity of the problems we work on and the data-rich environment of our business make it possible and essential to bring the tools of multiple disciplines to bear on our hardest problems. We are looking for an experienced team manager for Growth Algorithms to lead a talented group of data scientists responsible for optimizing client acquisition, retention, and reactivation. The team works across the client lifecycle—from paid media and onboarding to site experiences, CRM, incentives, and reactivation—using experimentation, predictive modeling, and personalized recommendations to help clients discover value and build lasting relationships with Stitch Fix. This is a highly cross-functional leadership role. This leader will bring a compelling data science vision that gives the team focus and inspires partners, while building trusted relationships across Product, Marketing, Engineering, Finance, Design, Enterprise Analytics, and other Algorithms teams. This leader will emphasize technical excellence while influencing business strategy, improving decision quality, challenging historical constraints, and concentrating the team’s capacity on work with the highest client and business value.

Requirements

Bachelor’s degree in a quantitative field such as Computer Science, Statistics, Economics, Physics, Mathematics, Operations Research, or a related field required; master’s degree or PhD preferred.
5+ years of experience applying machine learning, experimentation, and causal inference to business problems, ideally across growth, lifecycle engagement, marketing, personalization, e-commerce, or retail.
2+ years of experience as a technical team lead or direct people manager, with a track record of developing strong talent and delivering meaningful outcomes through others.
Deep practical expertise in experiment design and interpretation, including metric design, statistical power, sources of bias, and decision-making under uncertainty.
Strong cross-functional leadership skills, including experience defining analytics ownership, decision rights, and best practices across Data Science, Product, Engineering, Marketing, and Analytics teams.
A track record of creating a compelling data science strategy, translating it into a focused portfolio, and helping a team understand how its work connects to client and business outcomes.
Demonstrated willingness to challenge assumptions, simplify complex systems or processes, and stop or consolidate lower-value work while leading change with transparency and empathy.
Technical fluency to guide full-stack data science across analysis, experimentation, machine learning, system design, and production-quality Python code—and to raise quality through review, questions, and coaching.
Excellent communication skills, with the ability to explain evidence, uncertainty, tradeoffs, and recommendations to executives, business partners, and technical audiences.
Sound judgment about where traditional machine learning, generative AI, AI-assisted development, and ad hoc analytical approaches create optimal value.

Responsibilities

Set and communicate a clear data science vision for Growth Algorithms, turning acquisition, activation, retention, and reactivation goals into a focused portfolio.
Act as a trusted thought partner across Product, Marketing, Engineering, Finance, Design, and Enterprise Analytics; define clear ownership and shared best practices for experimentation, product analytics, AI/ML measurement, and decision support.
Raise the bar for experimental design and interpretation, ensuring plans and outcomes reflect both statistical evidence and business context; turn uncertainty and tradeoffs into clear recommendations.
Guide strategy for Next Best Action, lifecycle personalization, predictive value, CRM and paid media, onboarding funnel, and incentives.
Challenge historical constraints and reframe persistent problems, establishing new technical, analytical, product, or operating approaches when established paths fall short.
Make hard portfolio choices with rigor and care: consolidate overlapping capabilities, retire low-value work, reduce legacy complexity, and focus capacity on the highest-impact opportunities.
Lead a team of full-stack data scientists who own work from opportunity sizing and experiment or model design through production deployment, measurement, monitoring, on-call, and incident response; partner with Engineering on reliable systems and choose the simplest effective approach.
Build an inclusive, high-performing team grounded in ownership, candid feedback, curiosity, kindness, and continuous learning.