• Own the product vision, strategy, and roadmap for Trade Compliance and Export Controls within GET • Translate regulatory obligations and risk scenarios into clear product outcomes and priorities • Balance short‑term regulatory commitments with long‑term platform scalability and reuse • Partner with engineering teams to design automated, scalable, and auditable compliance capabilities • Drive integration of compliance enforcement across ERP, CRM, Subscription, Commerce, and acquisition platforms • Define end‑to‑end transaction workflows spanning order capture, fulfillment, invoicing, and renewals • Act as the primary product interface for Legal, Compliance, Finance, Commerce Operations, and Engineering • Align senior stakeholders on scope, sequencing, and tradeoffs • Influence upstream systems and data owners to improve product classification, screening accuracy, and data quality • Author clear product requirements, epics, and acceptance criteria • Partner closely with engineering using Agile delivery practices • Prioritize work to meet regulatory deadlines while minimizing customer and operational friction • Ensure enforcement actions are traceable, explainable, and audit‑ready • Support internal and external audits through product capabilities and reporting • Define repeatable playbooks for integrating acquired companies into compliant transaction workflows

• Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine). Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance. • Translate business problems into ML hypotheses and solutions. Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes. • Design and execute rapid, pragmatic validation cycles. Formulate clear hypotheses (Problem → Mechanism → Impact → Metric), choose the right validation method (A/B test, shadow model, phased rollout), and make data-driven go/no-go decisions under uncertainty. • Apply critical thinking to decompose complex, ambiguous problems. Cut through noise, prioritize what truly matters, and build simple, effective solutions first. • Collaborate closely with Engineering, Data Science, and business teams. Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.

• Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine). • Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance. • Translate business problems into ML hypotheses and solutions. • Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes. • Design and execute rapid, pragmatic validation cycles. • Formulate clear hypotheses (Problem → Mechanism → Impact → Metric), choose the right validation method (A/B test, shadow model, phased rollout), and make data-driven go/no-go decisions under uncertainty. • Apply critical thinking to decompose complex, ambiguous problems. • Collaborate closely with Engineering, Data Science, and business teams. • Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.

• Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine). • Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance. • Translate business problems into ML hypotheses and solutions. • Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes. • Design and execute rapid, pragmatic validation cycles. • Formulate clear hypotheses (Problem → Mechanism → Impact → Metric) and choose the right validation method (A/B test, shadow model, phased rollout). • Apply critical thinking to decompose complex, ambiguous problems. • Collaborate closely with Engineering, Data Science, and business teams. • Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.