Senior Full-Stack Data Scientist, Personalization

Socket.dev; Northern (KY); Hybrid; USD 180,000 - 205,000; Full time

Job summary

Stitch Fix is redefining retail by blending the art of fashion with the science of machine learning. The Client Experience Algorithms organization builds tools that power personal style, using models and human judgment.

On the Growth Algorithms team, you’ll apply multidisciplinary tools to solve problems in a data-rich environment, directly influencing client acquisition, retention, and reactivation programs.

Qualifications

• Bachelor’s degree in a quantitative field; Master’s degree or PhD preferred.
• 5+ years designing and deploying ML algorithms, ideally in retail personalization.
• Ability to architect solutions and write production-grade Python applications.
• Experience with large-scale distributed datasets and bias handling.
• Applied knowledge of AI-assisted coding and agentic product solutions.
• Experience with online A/B testing and performance metrics.
• Strong communication and ownership mindset.
• Team player with growth mindset.
• Willingness to take on new challenges and learn from failure.

Responsibilities

• Work in a team that embraces both LLMs and traditional ML.
• Design experiments and communicate results to stakeholders and leadership.
• Help optimize client journeys through Next Best Action modeling and CRM.
• Leverage data about clients, merchandise, and interactions.
• Collaborate with product, design, and engineering to roadmap new features.

Skills

ML design & deployment

Python

A/B testing

Data analysis

Communication skills

Education

Bachelor’s degree in quantitative field

Master’s or PhD preferred

Tools

Python

Experimentation frameworks

Job description

Stitch Fix is redefining retail by blending the art of fashion with the science of machine learning. The Client Experience Algorithms organization builds tools that power personal style, using models and human judgment.

On the Growth Algorithms team, you’ll apply multidisciplinary tools to solve problems in a data-rich environment, directly influencing client acquisition, retention, and reactivation programs.