• Planejamento, acompanhamento e governança dos projetos • Estruturar o roadmap de produtos • Conduzir estudos/análises para decisões embasadas

• Business Ownership of Data Across the Value Stream • Responsible for business decisions related to data used across the product value stream • Collaborate with Value Stream Leader(s), P&C Leadership, and other stakeholder’s to understand value stream data needs • Understand how data flows across systems, products, and processes • Assess business impacts, dependencies, and tradeoffs of data‑related decisions • Enabling Product Teams with Data • Facilitate cross P&C discussions to define Data Product standards and designs, in close collaboration with Product Owners and Architects • Partner with Product Owners to co-own identification and prioritization of all data-related work across Agile PODS initiatives and features with support from P&C Leadership • Support teams to make sure they have data to measure value, outcomes, and adoption • Enable consistent, fact‑based planning, prioritization, and review discussions • Partnership with IT and Enterprise MDM • Work closely with the IT Data Product Owner, Data Architects, and Enterprise Master Data Management team to articulate data business context and definitions • Collaborate on technical design options, sequencing, and feasibility assessments • Balance business priorities with architectural standards and platform constraints • Stakeholder Engagement & Decision Support • Engage business stakeholders to understand decision needs and data requirements • Synthesize inputs to support transparent, value‑based decisions • Communicate impacts and tradeoffs of data decisions clearly to leadership • Data Quality, Governance & Adoption (Business Perspective) • Champion business definitions, data quality standards, and usage expectations • Partner with IT and governance teams to ensure data is trusted and consistently interpreted • Ensure IT Commercial Data Delivery and Enterprise MDM are governing necessary data updates, maintaining data quality and data accuracy according to defined data business context • Drive adoption by ensuring data aligns to real business decisions and workflows • Collaborate with the Architects to drive Master Data improvements.

• Own AI product strategy for investor workflows: Define how AI transforms the way public market investors consume, analyze, and act on data. • Build AI-native products, not just features: Design end-to-end experiences across agents, copilots, and structured outputs, not just UI layers on top of models. • Translate frontier AI into real-world utility: Evaluate and integrate LLMs, retrieval systems, and agent architectures into production-grade products. • Work directly with customers: Partner with hedge funds and asset managers to deeply understand workflows, pain points, and trust requirements. • Drive rapid experimentation and iteration: Run tight feedback loops across prompts, evals, and product UX to continuously improve performance and usefulness. • Collaborate across disciplines: Work closely with engineering, data, research, and design to ship high-quality, high-impact products, while communicating clearly with both technical and non-technical partners. • Define quality, reliability, and trust: Establish evaluation frameworks for AI outputs, ensuring accuracy, consistency, and investor-grade reliability. • Shape the future of the platform: Contribute to how MCPs, agents, and APIs evolve into a unified, reliable, and safe AI product ecosystem.

• Help define product vision and strategy while owning the roadmap to deliver on the strategy, aligned to customer needs, company priorities, and growth goals. • Conduct customer discovery, market research, and competitive analysis to identify opportunities in workflow improvement, analytics, reporting, and data monetization. • Own product initiatives with support from product and cross-functional leaders, partnering with engineering, design, and data teams to translate business needs into product requirements, user stories, and measurable outcomes. • Support prioritization decisions across competing opportunities, balancing customer value, technical feasibility, strategic fit, and commercial impact. • Use quantitative and qualitative data to evaluate product performance, customer behavior, feature adoption, retention, and expansion opportunities, and translate insights into action. • Partner with sales, marketing, and customer success teams on go-to-market planning, customer enablement, and launch readiness for new capabilities. • Prepare business cases, performance updates, and product recommendations grounded in product, customer, and revenue data.