About HighLevel:HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.
To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.
Behind the platform, HighLevel 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.
Our PeopleWith over 2,000 team members across 10+ countries, HighLevel 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.
Our ImpactEvery month, HighLevel 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.
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About the Role:
We're hiring a Lead Product Analyst, AI Products to be the analytics owner and measurement lead for HighLevel'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.
Responsibilities:
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
Requirements:
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 influence -you shift PM priorities without direct authority in a fast-moving environment
Nice to Have:
Experience measuring AI/ML or LLM-based product features, including non-deterministic systems
B2B SaaS, CRM, or product-led growth background
Experience with conversational, voice, or agent/assistant products
Familiarity with Statsig or a comparable experimentation platform
Exposure to AI-assisted analytics workflows
Success in this role looks like:
The AI surfaces are instrumented to a standard that makes them measurable, with coverage gaps closed on a known schedule
Each AI product has agreed, trusted feature-level KPIs and success metrics everyone uses
Adoption and quality of AI features are understood by segment, not guessed at
AI experiments are designed and read rigorously despite non-deterministic behavior
AI PMs make roadmap calls off the analysis, and the pillar has a foundation the next analysts can build on
