Define and execute a multi-year product vision and roadmap for search relevance.
Partner closely with data science, engineering and digital stakeholders to define requirements, prioritize initiatives, and deliver measurable business impact.
Translate complex data science and search concepts into clear business and athlete value.
Establish KPIs to measure value realization and impact of search data science capabilities.
Define and monitor metrics to continuously improve search relevance and model performance.
Partner with analytics and optimization teams to design and execute A/B tests across digital channels.
Own prioritization and tradeoff decisions across search relevance models, experimentation strategy, and platform investments.
Requirements
7+ years in product management.
4+ years focused in building machine learning data products for search.
Proven track record building data solutions at enterprise scale.
Extensive familiarity with search engines (e.g., Elasticsearch, Solr or similar) and ecommerce APIs.
Strong fluency in ecommerce analytics and customer behavior metrics.
Deep understanding of ecommerce A/B testing methodologies to drive learning and iterative development.
Strong technical fluency and ability to discuss development of data science models and pipelines, data architecture, and integration with search APIs.
Expertise defining and measuring product KPIs that tie to business outcomes.
Familiarity with Agile/Scrum methodologies and product management tools (Jira, Confluence, Aha!, Miro).
Strong ability to translate complex technical concepts into executive-ready narratives and business value propositions.
Effective facilitation, relationship building and collaboration skills to drive alignment across organizational peers and stakeholder groups.
Experience writing user stories, managing backlogs, and leading agile ceremonies.
Tech Stack
ElasticSearch
Benefits
Competitive total rewards package that could include other components such as: incentive, equity and benefits.