• Own the product strategy and execution for the ecosystem surfaces: home, sharing, creative profiles, follow and discovery mechanics, logged-out web landing pages, and paiement flows. • Run a continuous discovery loop: user interviews, session replays, usability tests, focused on the moments that happen before and after the core creative experience. • Partner closely with design to shape flows from first concept through to shipped experience. You have a strong enough design eye to catch what is off before it reaches engineering. • Drive delivery alongside engineering: write crisp specs, run sprints, track progress, hold the quality bar. • Track performance across your surfaces using our analytics platform, own event instrumentation, and use data to make sharp prioritization calls. • Shape the product side of growth: identify the funnel gaps, propose the experiments, and surface the changes that move the needle. • Use modern tools: AI assistants, research synthesizers, internal tooling, as a core part of how you work.

• Own the product vision, strategy, and roadmap for agentic AI solutions that reduce out-of-stocks, shrink and waste, and improve fulfillment utilization for retailers and CPGs. • Work backwards from customer outcomes to write crisp problem statements, PRDs, and success metrics; prioritize ruthlessly to deliver v0, iterate to v1, and scale proven features. • Partner with engineering and data science to scope experiments, manage backlogs, define KPIs and evaluation frameworks, and ensure models and agents are reliable, safe, and observable. • Lead discovery with customers; design pilots and proof points; collaborate with sales and marketing on go-to-market, pricing and packaging, enablement, and case studies that drive revenue. • Own the business case: model unit economics and ROI, set financial targets for margin recovery/efficiency, and report performance to executives and external stakeholders. • Ensure data readiness and integrity by assessing data quality, telemetry, integrations, and feedback loops; implement human-in-the-loop and policy guardrails where appropriate. • Operate cross-functionally with data engineering, retailer success, enterprise sales, and BD to bring products from concept to launch and expansion.

• Own the product vision, strategy, and roadmap for agentic AI solutions that reduce out-of-stocks, shrink and waste, and improve fulfillment utilization for retailers and CPGs. • Work backwards from customer outcomes to write crisp problem statements, PRDs, and success metrics; prioritize ruthlessly to deliver v0, iterate to v1, and scale proven features. • Partner with engineering and data science to scope experiments, manage backlogs, define KPIs and evaluation frameworks, and ensure models and agents are reliable, safe, and observable. • Lead discovery with customers; design pilots and proof points; collaborate with sales and marketing on go-to-market, pricing and packaging, enablement, and case studies that drive revenue. • Own the business case: model unit economics and ROI, set financial targets for margin recovery/efficiency, and report performance to executives and external stakeholders. • Ensure data readiness and integrity by assessing data quality, telemetry, integrations, and feedback loops; implement human-in-the-loop and policy guardrails where appropriate. • Operate cross-functionally with data engineering, retailer success, enterprise sales, and BD to bring products from concept to launch and expansion.

• Own the roadmap for your product stream, prioritizing features based on business goals, customer needs, and market trends • Define clear product requirements that deliver value in releasable pieces • Leverage AI for impact by synthesizing customer research, drafting PRDs, pressure-testing ideas, and producing better documentation without sacrificing depth • Lead ongoing customer research to deeply understand user pain points, workflows, and unmet needs, and translate those insights into actionable features • Make data-informed decisions to track performance, validate hypotheses, and identify areas for improvement • Stay close to the market, monitoring competitor moves and industry trends to identify where Bridgit can lead and differentiate • Keep stakeholders aligned, proactively surfacing risks and trade-offs before they become problems • Become the expert on Bridgit, developing deep fluency in the data, workforce planning workflows, and customer context that drive the product