Lead Product Analyst - AI Products

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 People With 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 Impact Every 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. Learn more about us on our YouTube Channel or Blog Posts.

About the Role:

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

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