Lead Product Analyst - AI Products

High Level is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, High Level supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on High Level have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure  centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.

Behind the platform, High Level powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.

With over 2,000 team members across 10+ countries, High Level operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.

Every month, High Level enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that. Learn more about us on our You Tube Channel or Blog Posts.

We're hiring a Lead Product Analyst, AI Products to be the analytics owner and measurement lead for High Level's AI pillar
-Voice AI, Conversation AI, AI Employee, and Ask AI. These products are shipping fast with limited analytics foundation underneath them; we would provide value by creating consistent instrumentation, agreed success metrics, and a rigorous read on adoption or quality. You'll build that from the ground up. Your first job is to define the feature-level KPIs and success metrics each AI product is judged on.

From there you'll own adoption and quality analysis and, partnering closely with the Experimentation & Causal Inference lead, bring rigor to measuring non-deterministic, fast-iterating AI features where a classic A/B read is hard. You'll work amid a data foundation still being built, consuming governed sources rather than rebuilding them, and raising the bar as you go. As the AI analytics lead, you set the measurement direction and standards for the domain and develop the analysts who join as it scales.

• Own instrumentation coverage for the AI pillar
  -define the events and properties each AI surface must emit, and drive them into the roadmap with PMs and engineering

• Define feature-level KPIs and success metrics for Voice AI, Conversation AI, AI Employee, and Ask AI
  -what "working" and "adopted" mean, made explicit and trusted

• Partner with the Experimentation & Causal Inference lead to design and read AI experiments, including measurement approaches for non-deterministic, fast-iterating systems

• Separate real signal from instrumentation gaps, novelty effects, and data maturity in every read

• Set the measurement direction and standards for the AI analytics domain
  -the canonical metrics and instrumentation contracts other teams build on
  -and mentor analysts as the pod grows

• Translate findings into clear recommendations for AI PMs and influence the roadmap without owning it

• Flag data gaps to Analytics Engineering and help shape the event taxonomy AI analytics depends on

• Use AI tooling (Claude and similar) to move faster on exploration, documentation, and analysis

• 7+ years in product analytics, data science, or applied statistics, with hands-on ownership of a product area's metrics end to end

• Strong instrumentation instinct
  -you've defined event tracking/taxonomy and driven it into a product roadmap, not just consumed existing tables

• Ability to define success metrics for a product from a standing start and get stakeholders to adopt them

• Fluency partnering on experiments (A/B design, guardrails) and interpreting results honestly

• Strong SQL and working proficiency in Python; comfort in a Snowflake + dbt environment

• Comfort working amid imperfect, in-progress data
  -you consume governed sources and help raise the bar rather than rebuilding pipelines

• Cross-functional…