Senior Product Manager, Search and Discovery

Job Description

Job Description

About Yami:

Founded in 2013, Yami’s mission is to bring the world closer for everyone to experience and enjoy. We make it easy to discover exciting flavors and trending products from Asia. Named Inc. Magazine’s fastest-growing start-up on the ”Inc. 500 List.”, we’re committed to connecting people with authentic food, beauty, home, and wellness experiences through our e-commerce platform, iOS, and Android apps.

Benefits & Compensation:

• 401(k) matching
  

• Health insurance: medical, vision, and dental
  

• $100K-135K
  

• Paid time off (PTO): vacation, sick, and holidays
  

• On-site gym/pool and game rooms
  

• Employee discount
  

• Coffee and snacks
  

Description

Senior Product Manager, Search and Discovery will own the systems and product experiences that help customers efficiently find, explore, and engage with the right products across the platform.

This role defines product strategy, roadmap, and delivery plans by working backward from the search and discovery strategy and targeted business outcomes, including improvements in search relevance, query understanding, conversion rate, click-through rate, null/low result rate, search-driven GMV, and overall customer satisfaction.

Partnering closely with Engineering, Data Science, Analytics, Merchandising, UX Design, and Content, you will drive the build and evolution of end-to-end search and discovery capabilities across query understanding and intent recognition, search ranking and relevance optimization, autocomplete and query suggestion, browse and navigation taxonomy, personalized search and recommendation, search analytics and performance dashboards, content discovery and merchandising integration, and search result presentation (trust signals, product differentiation, and information architecture) — with clear visibility, ownership, and reliable follow-through.

Job responsibilities: 

• Own the end-to-end product strategy and roadmap for search and discovery systems
  

• Drive improvements in key metrics such as search relevance, CTR, conversion rate, null/low result rate, and search-driven GMV
  

• Lead the development of core capabilities, including:
  

• Query understanding and intent recognition
  

• Search ranking and relevance optimization
  

• Autocomplete and query suggestions
  

• Browse and navigation taxonomy
  

• Personalized search and recommendations
  

• Search analytics and performance dashboards
  

• Content discovery and merchandising integration
  

• Search result presentation (trust signals, product differentiation, information architecture)
  

• Partner closely with Engineering and Data Science teams to define model objectives, evaluate performance, and translate ML capabilities into product features
  

• Collaborate with Merchandising, Content, and UX teams to enhance product discovery and user experience
  

• Establish clear success metrics, conduct root-cause analysis, and translate insights into actionable product improvements
  

• Drive alignment across cross-functional stakeholders and ensure strong execution across distributed teams
  

Basic Qualifications

• 5+ years of product management experience delivering scaled search, discovery, or recommendation systems in e-commerce, marketplace, or content platform environments.
  

• Deep understanding of search and information retrieval concepts, including query parsing, intent classification, ranking algorithms, indexing, recall and precision trade-offs, and relevance tuning.
  

• Knowledge of key search and discovery metrics, including CTR, conversion rate, null result rate, MRR (Mean Reciprocal Rank), NDCG (Normalized Discounted Cumulative Gain), search exit rate, add-to-cart rate, and search-attributed GMV.
  

• Strong analytical and structured problem solving: root-cause analysis, clear problem framing, success metrics definition, and translation of insights into system capabilities and operational mechanisms.
  

• Experience working with machine learning and data science teams to define model objectives, evaluate model performance, and translate ML capabiliti