AI​/ML Applied Scientist

About This Team

The Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and products that enable the enterprise to operate with intelligence  team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon’s vertically integrated retail ecosystem, embedding strong data governance and responsible AI practices from the very beginning.

By applying AI to critical business challenges and creating new, transformative AI solutions, the team helps reshape how lululemon operates. Through deep partnership with product, technology, and business teams, Enterprise Data & AI accelerates product innovation, unlocks measurable value, elevates guest and educator experiences, and drives enterprise efficiency.

As a Staff Applied AI/ML Scientist, you define, lead, and scale AI/ML solutions aligned to business outcomes. You provide technical leadership and direction, design the solution for problems with high system complexity and multiple components. Your solutions model enterprise standards and lifecycle practices and ensure solutions are scalable, reliable, and ethically applied. You lead complex initiatives across teams, influence technology decisions, and embed solution‑oriented thinking across AI/ML investments.

You operate as a technical authority for applied AI/ML methodology and state‑of‑the‑art, setting scientific direction for complex problem domains.

• Designing and deploying machine learning and predictive/generative AI models for demand forecasting, optimization, personalization and search, intelligent automation, and multimodal content creation and enrichment
• Applying NLP, computer vision, multimodal, or time‑series modeling techniques to enterprise‑scale data sets that are a mix of structured and unstructured data
• Establishing frameworks for product ionizing AI solutions and how AI/ML model performance translates into business decisions
• Acting as a senior technical partner to product, engineering, and business leaders on AI feasibility, risk, and value realization across a multi‑year roadmap

• Establish AI/ML solution frameworks for how specific business domains apply AI/ML to business problems, and develop production‑ready AI/ML models and solutions that deliver business value
• Drive the technical design and delivery of AI/ML solutions for complex business problems, applying advanced techniques to deliver measurable business impact, through production‑ready models that scale effectively
• Guide best practices defining how the organization transforms data into intelligent systems through AI/ML, designing AI/ML pipelines in partnership with software engineering teams
• Establish experimentation standards defining rigorous evaluation approaches, business impact measurement, and validation practices across organization
• Drive business domain integration ensuring AI/ML solutions are informed by business context, stakeholder needs are met

• Bachelor’s degree in computer science, mathematics, or related field;
  Master’s or PhD preferred
• 9‑12 years of AI/ML research in industrial/corporate setting, or equivalent research experience, including educational experience (e.g., a PhD degree)
• Demonstrated experience in technically leading applied AI/ML solution delivery for high‑impact business problems where solution architecture, model choice, evaluation rigor, quality, and trade‑off decisions materially affect outcomes
• Experience designing complex AI/ML solutions with multiple models and components and deploying them to production
• Experience guiding how the team evaluates and adopts emerging and state‑of‑the‑art methods at scale for specific use‑cases
• Experience establishing domain‑wide frameworks for AI/ML solution design and implementation including development standards, evaluation criteria, and responsible AI requirements adopted across teams
• Experience with ML implementation using commonly used tools such as Python, PyTorch, cloud ML platforms and libraries, open‑source toolkits, and…