• Own the product strategy and execution for Tala’s embedded lending partnerships globally • Assess which opportunities are worth pursuing, design the right credit solution for each partner context, shape commercial deals from a product perspective, and lead launches end to end • Work in close collaboration with the GTM team, be the product voice in partner conversations and responsible for what gets built, launched, and grown

• Define the AI specialist: what it knows, how it engages, where it draws the line, and when it hands off. Own the product vision, strategy, and roadmap. • Launch and scale the inbound motion across chat, voice, and email for prospects and new users seeking help with products, pricing, and getting started. Drive rapid iteration on answer quality and resolution rates. • Build the triage and qualification layer. The AI specialist must assess the user it is engaging with, understand the size and complexity of the opportunity, and route accordingly: guiding smaller users through their full journey while qualifying larger prospects and handing them to sales with rich context. • Define escalation paths to the vendor support team and sales team. Ensure that handoffs are seamless, context is preserved, and no user interaction is left unresolved. • Scale past the first interaction into onboarding and cross-sell, helping users activate products, discover new ones, and grow their usage of Stripe. • Leverage Stripe's existing product recommendation, merchant context, onboarding systems, and outreach platform rather than rebuilding from scratch. • Own the revenue this motion produces. Define success metrics and maintain accountability from conversion through expansion, including pipeline generated through AI-qualified leads. • Partner with engineering, AI/ML, design, data science, marketing, sales, and support to execute at pace while maintaining Stripe's bar for quality and trust.

• Discovery & Assessment: Review legacy data across historical and current systems. Document existing data structures, definitions, flows, and constraints. Identify risks, gaps, and limitations impacting the conversion. • Data Mapping: Define detailed business and functional data conversion requirements. Develop source-to-target mapping documents and data dictionaries. Facilitate SME validation and requirement sign‑off. • Migration Planning: Collaborate with technical teams to define migration approach, sequencing, and transformation logic. Develop conversion testing scenarios and acceptance criteria. Support cutover planning and quality controls. • Execution Support: Participate in conversion execution cycles. Review data exceptions and support defect resolution. Validate completeness, accuracy, and adherence to business rules. • Governance & Documentation: Ensure alignment with enterprise data governance standards. Collaborate with IT Data Architect. Maintain complete documentation of conversion logic, lineage, and decisions. Provide regular reporting to project leadership.

• Own your product surface end to end — deciding what gets built, when it ships, and how it serves your users or consuming teams, with full accountability for scope, priority, and quality. • Produce engineering-ready specifications for all features — complete user flows, visual designs, data requirements, edge cases, and acceptance criteria — using AI tools as a core part of your workflow; engineering builds from your specifications directly, without ambiguity about what the experience should be. • Make priority decisions and defend them — customer success, sales, and leadership will bring you requests; you decide which enter the roadmap and which don’t, with clear reasoning and transparent communication. • Maintain deep fluency with your customers — whether they are law enforcement practitioners, agency administrators, internal platform consumers, or data science teams — through direct engagement, user research, and ongoing relationship-building. • Conduct or commission user research as needed — interviews, usability testing, workflow observation, product analytics review — and engage internal subject matter experts to ground product decisions in direct evidence rather than assumptions. • Partner directly with your engineering pair as a trio; bring clarity on what to build and why while they bring clarity on how to build it and what is feasible. • Own post-launch adoption and feedback — evangelize new capabilities, train internal teams, track feature adoption, gather user feedback, maintain documentation, and iterate based on real-world usage rather than handing off after launch. • Support enterprise sales conversations with substantive product and domain knowledge when needed — not just feature lists, but the depth that builds customer confidence. • Leverage AI tools and technologies across all aspects of your work — research, specification, design, analysis, prototyping, and documentation.