Lead Product Manager, Data & Analytics

San Jose/ San Francisco Bay Area

interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI.

Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth.

interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise, interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation.

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.

Key Responsibilities

Internal Data Platform & Architecture

  • 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.

Customer-Facing Analytics Product

  • 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.

Insight Products Across Product Lines

  • 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.

Conversational & AI-Powered Interfaces

  • 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.

Product Analytics & Experimentation

  • 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.

What Success Looks Like

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.

Qualifications

Required

  • 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

Preferred

  • 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

Why This Role is Strategic

  • 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.

Benefits

  • Health: medical, dental, and vision insurance and wellbeing resources and programs
  • Time away: Public holidays and discretionary PTO package for flexible days off with manager approval
  • Financial: 401K, ESPP, Basic life and AD&D insurance, long-term and short-term disability
  • Family: parental leave
  • Development: Access to internal professional development resources.

At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not  discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.

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