Senior Product Manager, Data & AI Products (R14077)

ABOUT OPORTUN Oportun (Nasdaq: OPRT) is a mission-driven financial services company that puts its members' financial goals within reach. With intelligent borrowing, savings, and budgeting capabilities, Oportun empowers members with the confidence to build a better financial future. Since inception, Oportun has provided more than $21.3 billion in responsible and affordable credit, saved its members more than $2.5 billion in interest and fees, and helped its members set aside an average of more than $1,800 annually. WORKING AT OPORTUN Working at Oportun means enjoying a differentiated experience of being part of a team that fosters a diverse, equitable and inclusive culture where we all feel a sense of belonging and are encouraged to share our perspectives. This inclusive culture is directly connected to our organization's performance and ability to fulfill our mission of delivering affordable credit to those left out of the financial mainstream. We celebrate and nurture our inclusive culture through our employee resource groups. POSITION OVERVIEW We're looking for a Senior Product Manager to own and scale Oportun's internal data products and lead our Gen AI analytics initiative. This role sits at the intersection of data infrastructure, AI/ML, and product strategy. You'll own critical data products that power Capital Markets, FP&A, and Enterprise Analytics while building the next generation of AI powered data access tools. You'll be building internal products that make Oportun's data more accessible, trustworthy, and actionable. Your users are analysts, data scientists, finance and other business teams, and executives who depend on data to make high-stakes decisions every day. If you're excited by the idea of applying product thinking to data infrastructure and AI systems, and you want to work on problems that span forecasting models, capital markets operations, semantic layers, and agentic AI, this role is for you. RESPONSIBILITIES Gen AI Analytics Initiative (OportunResearch) You'll lead product strategy for OportunResearch, our AI-powered analytics assistant. This means defining the roadmap, working with engineering on agentic capabilities, designing human-in-the-loop workflows, and driving adoption across the org. Data Products Portfolio You'll own the product strategy for EDT's core data products: Merlin: Our suite of forecasting models powering Collateral Revenue, Fair Value, and related corporate forecasting. You'll drive the roadmap from current state through advanced time-series capabilities. Cosmos: Our Capital Markets data platform handling pledging operations, concentration management, and investor reporting. HITL Platform: The Human-in-the-Loop system for capturing institutional  knowledge, validating AI outputs, and building feedback loops that make our data and AI systems smarter over time. Semantic Layer & Data Certification You'll partner with analytics engineering to ensure our semantic layer is comprehensive enough to support AI-powered querying. This means understanding metric definitions, data certification status, and how to expose the semantic layer as an API for AI agents. What Success Looks Like In the first 90 days: Deep understanding of Merlin, Cosmos, and the HITL platform architecture and roadmaps Relationships built with key stakeholders across Capital Markets, FP&A, Risk, and Engineering First iteration of OportunResearch product requirements and pilot scope defined In the first year: OportunResearch launched and demonstrating measurable reduction in analyst Merlin advanced forecasting capabilities (scenario planning, time-series optimization) in production HITL platform operational with feedback loops capturing institutional knowledge Clear metrics showing self-service adoption growth and response quality REQUIREMENTS Experience: 6-10+ years of product management experience, with at least 3 years focused on data products, analytics platforms, or AI/ML systems Track record of shipping data-intensive products in complex enterprise environments Experience working with data engineering, analytics engineering, and/or ML engineering teams Familiarity with the modern data stack (warehouses, semantic layers, BI tools, orchestration) Technical Fluency: You don't need to write production code, but you should be able to read SQL, understand data modeling concepts, and have informed conversations about system architecture Understanding of LLMs, RAG patterns, and agentic AI concepts (you don't need to be an ML engineer, but you need to understand what's possible and what's hype) Comfort with ambiguity in fast-moving technical domains Product Skills: Strong product discovery skills: ability to understand user needs, synthesize research, and define problems worth solving Ability to write clear product requirements that engineering teams can execute against Experience defining and tracking metrics that matter, not vanity metrics Skill at managing competing priorities and making tradeoffs explicit Collaboration & Communication: Ability to influence without authority across engineering, analytics, and business teams Clear written and verbal communication, especially when explaining technical concepts to non-technical stakeholders Comfort presenting to executives and defending product decisions Mindset: You're energized by hard problems that don't have obvious solutions You're comfortable building for internal users who have high expectations and strong opinions You believe that data products deserve the same rigor and craft as consumer products You're not precious about your ideas; you'd rather be right than be the one who came up with the answer Nice to Have Experience in financial services, fintech, or capital markets Experience building AI/ML products, especially LLM-powered applications Familiarity with Databricks, db