Lead Product Manager- Data & Analytics

interface.ai is the most advanced Agentic AI platform built for credit unions and community banks. Trusted by over 100 institutions and serving millions of daily interactions, we help financial institutions automate operations, elevate CX, and scale financial wellness with AI-first solutions across chat, voice, and internal copilots.

As a fast-growing, venture-backed, product-led company, we operate at the frontier of AI, fintech, and enterprise software—transforming how banks serve their customers and run their operations.

As Lead Product Manager – Analytics, you will own our entire data and intelligence ecosystem. This includes:

• Our internal data science and data platform strategy
  
• Our customer-facing analytics product used by 100+ financial institutions to understand automation, engagement, and operational ROI
  
• The product analytics function, responsible for measurement, instrumentation, and strategic insights across product teams
  

This is a foundational role—bridging AI, data infrastructure, and product strategy to help interface.ai and our customers become more intelligent, autonomous, and data-driven.

• Own and evolve our internal data architecture, including ingestion, transformation, data access, observability, and governance.
  
• Champion modern data paradigms—domain-oriented ownership, decoupled pipelines, and federated governance inspired by data mesh principles.
  

• Define and ship analytics features that power financial insights, agent performance, and automation metrics for end-users at banks and credit unions.
  
• Promote self-serve data exploration, usage visualizations, and institution-specific dashboards tailored for executive, operations, and support personas.
  

• Drive the creation of cross-product intelligence layers that combine voice, chat, and internal co-pilot data into unified narratives and predictive insights.
  
• Enable shared primitives (metrics libraries, KPI models, alerting policies) for Orbit, Sphere, Nexus, and Analytics teams to leverage.
  

• Build natural-language driven analytics experiences—where customers ask questions in plain English and receive relevant, contextual answers.
  
• Integrate with internal LLM and agentic systems to deliver intelligent summaries, auto-surfaced anomalies, and guided storytelling.
  

• Establish product analytics as a core function—define taxonomies, support event instrumentation, and enable cohort tracking and A/B testing.
  
• Ensure product teams have access to real-time data that supports better decisions, faster iteration, and continuous product-market alignment.
  

Within 6–12 months, you will:

• Launch a next-generation analytics experience used by both customers and internal teams.
  
• Define a trusted, scalable data model that supports reporting, experimentation, and conversational insights across all products.
  
• Operationalize product analytics frameworks across all squads—instrumentation, success metrics, retention analysis, and experimentation pipelines.
  
• Build insight features that combine structured and behavioral data into role-specific intelligence modules.
  

• 4–6 years of product management experience, with at least 2+ years in analytics and data platforms
  
• Engineering background: 2–3 years in software/data engineering and formal CS degree
  
• Experience owning modern data architecture or building analytics products that support both internal and external use cases
  
• Familiarity with data pipelines, event taxonomies, visualization frameworks, and privacy-safe data governance
  
• A product-led mindset: You treat analytics not as reporting, but as productized intelligence
  
• Skill in using prompt engineering techniques to enhance productivity, drive efficiency, and decision-making with generative AI tools.

• Experience building analytics tools in a B2B SaaS or fintech platform context
  
• Exposure to data mesh concepts, domain-oriented data ownership, and distributed analytics patterns
  
• Familiarity with LLM-driven summarization, auto-insight surfacing, or natural language data exploration
  
• Experience managing internal tooling for experimentation, growth analytics, or product success metrics
  

• You’ll define how data becomes productized intelligence—across institutions, internal teams, and platform primitives.
  
• You’ll build platform-wide insight systems that serve product, engineering, GTM, and customers.
  

You’ll operate at the intersection of data architecture, AI innovation, and user experience—bringing structure and value to every layer of the stack.

 Compensation:

• Compensation is expected to be between $180,000 - $210,000. Position has a bonus and Stock component. Exact compensation may vary based on skills and location.

What We Offer:

• 🏥 Medical/Dental/Vision Insurance
• 🏝 PTO & Holidays
• ✨ Life Insurance