Director Product Discovery and Recommendations

Overview

About PHOENIX

PHOENIX Retail, LLC is a retail platform operating the Express and Bonobos brands worldwide. Express is a multichannel apparel brand dedicated to a design philosophy rooted in modern, confident and effortless style whether dressing for work, everyday or special occasions. Bonobos is a menswear brand known for being pioneers of exceptional fit and a personalized, innovative retail model. Customers can experience our brands in over 400 Express retail and Express Factory Outlet stores, 50 Bonobos Guide shops, and online at  and

About Express

Express is a multichannel apparel brand dedicated to creating confidence and inspiring self-expression. Since its launch in 1980, the brand has embraced a design philosophy rooted in modern, confident and effortless style. Whether dressing for work, everyday or special occasions, Express ensures you look and feel your best, wherever life takes you.

The Company operates over 400 retail and outlet stores in the United States and Puerto Rico, the  online store and the Express mobile app.

Columbus Corporate Headquarters

The Director of Product Discovery and Recommendations will serve as the strategic architect of our AI/ML‑powered personalization ecosystem. This role will define, manage, and operate the product discovery, search relevance, and recommendation systems of our commerce and marketing touchpoints through intelligent product, placement, and targeting algorithms. You will partner with Engineering, Merchandising, and Content teams to deliver predictive and adaptive systems that drive engagement, conversion, and customer value.

You will integrate tried and true technologies such as collaborative filtering with cutting‑edge innovations such as semantic search and computer vision. This role is at the intersection of technology, data, merchandising, and customer experience.

• Strategic Roadmap & Execution: Define the long‑term vision for AI personalization. Orchestrate a hybrid ecosystem of proprietary internal data, AI/ML models, and 3rd party technology to drive Customer Acquisition, Retention, and LTV.
• Design and Implementation: Lead the end‑to‑end development of recommendation engines across customer touchpoints including commerce (homepage, PDP, PLP, Cart) messaging (Email, Clientelling) and Mobile App. Define relevance, ranking, and filtering algorithms to improve product discoverability and conversion.
• Technology Strategy: Actively scout, vet, and negotiate with AI/ML vendors. Lead Technology Assessments (RFPs) and Proof of Concept (PoC) testing to compare vendor performance against internal benchmarks.
• Experimentation & Growth: Drive incremental revenue through rigorous A/B testing across PLPs, PDPs, and lifecycle marketing. Design "champion‑challenger" tests to validate internal builds against external solutions.
• Algorithmic & LLM Optimization: Partner with data science to refine sort ranking models and launch LLM‑powered features (e.g., visually similar discovery), leveraging both internal expertise and external APIs.
• Omnichannel Orchestration: Architect a unified experience by leveraging cross‑channel marketing platforms to sync merchandise, commerce, and customer models with external marketing and CRM automation tools.
• Executive Stakeholder Management: Build business cases for investments in technology and translate complex algorithmic performance into actionable insights for C‑level leadership.

• Bachelor's Degree in Computer Science, IT or equivalent work experience.
• 10+ years of experience with personalization and recommendation systems.
• Scalable Impact: Proven track record of scaling personalization from pilot to 100% coverage, delivering significant incremental revenue (e.g., $100M+ scale).
• Hybrid Technical Depth: Deep understanding of ML recommendation techniques and the ability to audit both internal code and vendor "black‑box" models.
• Strong understanding of personalization architecture: collaborative filtering, content‑based recommendations, hybrid systems, and emerging AI/ML methodologies.
• Experience with A/B testing methodologies, experimentation…