• Understand customer needs and workflows across bioassays, functional assays, reagent-based assays, and related application areas. • Conduct voice-of-customer interviews with biopharma, CRO, academic, and assay development customers. • Analyze market trends, customer segments, competitive landscape, pricing dynamics, and application requirements. • Support product positioning, messaging, value proposition, and differentiation. • Work cross-functionally with R&D, sales, marketing, and regional teams to translate market needs into product and commercial plans. • Develop go-to-market materials, sales tools, customer-facing content, and launch support plans. • Identify KOLs, key accounts, and collaboration opportunities to support market development.

• Own the product roadmap for AI-powered transaction capabilities. Define, prioritize, and sequence features that bring intelligence to Lone Wolf’s transaction management workflows. Translate customer needs and technical possibilities into a roadmap that balances near-term impact with long-term platform value. • Drive customer discovery. Work directly with brokerages, agents, and transaction coordinators to understand how they manage documents, handle compliance, and deal with errors today. Identify the highest-value opportunities for AI to reduce friction and risk. • Ship and iterate. Partner with AI engineering to take capabilities from prototype to validated product. Write specs, define success metrics, run pilots, and iterate based on real usage data — not assumptions. • Manage the build-measure-learn loop. Design experiments to validate product-market fit for new AI capabilities. Track adoption, measure impact, and use data to inform what we build next. • Partner across the organization. Work closely with Lone Wolf’s existing product and engineering teams — particularly those owning transaction management, forms, and e-signature — to ensure Innovation work integrates cleanly and accelerates their roadmaps. Build trust and share learnings proactively. • Understand the compliance landscape. Real estate transactions involve regulated documents, financial data, and legal obligations. Develop fluency in the compliance requirements that shape how brokerages operate so that AI capabilities enhance compliance rather than creating risk. • Represent the customer inside Innovation. Be the voice of the broker, agent, and transaction coordinator. Ensure what we build solves real problems and fits into real workflows.

• Own the product roadmap for AI-powered transaction capabilities. Define, prioritize, and sequence features that bring intelligence to Lone Wolf’s transaction management workflows. Translate customer needs and technical possibilities into a roadmap that balances near-term impact with long-term platform value. • Drive customer discovery. Work directly with brokerages, agents, and transaction coordinators to understand how they manage documents, handle compliance, and deal with errors today. Identify the highest-value opportunities for AI to reduce friction and risk. • Ship and iterate. Partner with AI engineering to take capabilities from prototype to validated product. Write specs, define success metrics, run pilots, and iterate based on real usage data — not assumptions. • Manage the build-measure-learn loop. Design experiments to validate product-market fit for new AI capabilities. Track adoption, measure impact, and use data to inform what we build next. • Partner across the organization. Work closely with Lone Wolf’s existing product and engineering teams — particularly those owning transaction management, forms, and e-signature — to ensure Innovation work integrates cleanly and accelerates their roadmaps. Build trust and share learnings proactively. • Understand the compliance landscape. Real estate transactions involve regulated documents, financial data, and legal obligations. Develop fluency in the compliance requirements that shape how brokerages operate so that AI capabilities enhance compliance rather than creating risk. • Represent the customer inside Innovation. Be the voice of the broker, agent, and transaction coordinator. Ensure what we build solves real problems and fits into real workflows.

• Own the end-to-end product vision for the Data & ML squad, including the Forge AI creator agent and predictive performance infrastructure • Translate complex ML/data capabilities into user-facing product value for both brands and creators • Partner with Data Product Engineering leads to align data product strategy with OKRs and drive commercial outcomes (NDR, retention, contribution margin) • Define the metrics layer: determine what we measure, how we surface insights, and connect metrics to business impact • Identify and prioritize ML-powered bets (creator-brand match quality, content performance prediction, churn signals) and drive them from conception to launch