Lead Product Manager, Quince

• 6+ years of product management experience, with at least 3 years working closely with data science, ML, or search/information retrieval teams
• Proven track record of shipping and scaling customer-facing data science or search products with measurable business impact
• Strong foundation in experimentation (A/B testing, statistical methods, KPI definition) and ability to translate insights into product decisions
• Knowledge of machine learning productization: feature stores, inference platforms, model monitoring, and A/B testing of ML-driven systems
• Familiarity with search platforms and technologies (e.g., Algolia, Elasticsearch, Solr, OpenSearch) and understanding of their strengths/limitations
• Excellent written and verbal communication skills; able to influence cross-functional teams and executive stakeholders without formal authority
• Strong analytical mindset; comfortable digging into data and collaborating with engineers/data scientists to answer product questions with evidence
• Demonstrated success in defining roadmaps, prioritizing tradeoffs, and delivering high-quality product experiences

Desirable

• The Search Lead Product Manager is responsible for transforming onsite search from a keyword-based utility into an intelligent, personalized discovery engine
• In the near term, this role will focus on strengthening data platforms, experimentation, and incremental improvements on top of Algolia
• Over time, the Search Lead PM will drive the evolution of search toward semantic, AI-powered, and personalized experiences that differentiate our brand and delight customers
• Define and execute the search strategy balancing immediate platform improvements with long-term innovation
• Lead data platform and experimentation efforts (data pipelines, dashboards, A/B testing) to enable rapid iteration and insight-driven decision making
• Manage and improve current Algolia-based search, focusing on relevance tuning, query handling, and performance optimization
• Build roadmap for AI/semantic search evolution (vector-based retrieval, personalized ranking, LLM-assisted discovery)
• Partner cross-functionally with data science, data engineering, and ML engineering to deliver next-gen search models and pipelines
• Oversee QA, UAT, and signoff for new algorithms, features, and experimentation frameworks before production launch
• Champion voice of the customer by translating insights into actionable improvements across search relevance, usability, and speed
• Act as an internal thought leader on how AI and semantic technologies can reshape product discovery