Interface AI
Lead Product Manager, Data & Analytics
Interface AI, San Francisco, California, United States, 94199
Overview
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.
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 .
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.
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.
Equal Opportunity Interface AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and applicants. 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.
#J-18808-Ljbffr
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.
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 .
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.
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.
Equal Opportunity Interface AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and applicants. 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.
#J-18808-Ljbffr