About the Role:

We're hiring our first Staff Data Scientist, Experimentation & Causal Inference to define how HighLevel learns from experiments and turns results into trustworthy product decisions and business strategy. Our teams ship fast and have started experimenting to make data-backed decisions; you'll bring the rigor and consistency to scale that across the company.

You'll set the company-wide standard for experiment design and causal inference, embed it in how the product gets built, and coach PMs and analysts to run tests that hold up. You'll do this in a fast-moving, multi-product SaaS/CRM environment where samples are small, many products move at once, and a wrong "win" is costly. This is a founding, hands-on IC role with executive sponsorship and a path to build out a Data Science team as the function matures.

Responsibilities:

Define the end-to-end methodology every team follows - hypothesis → metrics → design → power → readout → decision - and make it the default
Own the statistical approach (significance, multiple comparisons, sequential testing, variance reduction like CUPED) for small-sample, fast-paced contexts where classic A/B power is hard to reach
Build the methods toolkit for our clustered, hierarchical data (user → sub-account/location → agency), where randomization and analysis units differ
Apply rigorous causal inference (matching, diff-in-diff, instrumental variables, synthetic control, etc) when clean experiments aren't feasible - churn, onboarding, GTM - separating real signal from selection bias, seasonality, and mix effects
Own the design discipline for running many experiments at once - layering, orthogonal experiments, holdouts, and guardrails that keep concurrent tests from contaminating each other
Partner with AI/ML teams to design and evaluate experiments for AI features, including measurement for non-deterministic, fast-iterating systems
Run the experiment review forum and hold the line on what counts as a real result
Build the Experimentation curriculum and templates that level up PMs and analysts so good design scales beyond you
Partner with Analytics Engineering on governed, experiment-ready data and consistent metric definitions
Influence leadership and cross-functional partners on where to invest, translating statistical nuance into clear, decision-grade guidance

Requirements:

9+ years in data science, product analytics, or applied statistics, with deep hands-on experience designing and analyzing online controlled experiments at scale
Strong applied statistics - frequentist foundations, Bayesian methods, power analysis, variance reduction, and the failure modes of A/B testing (peeking, multiple testing, network/cluster effects)
Practical causal inference, with sound judgment about when a result is causal versus an artifact of how the data was generated
Experience in small-sample, fast-paced, multi-product environments -you know when a decision needs a clean experiment and when it needs a fast, good-enough read
Strong SQL and working proficiency in Python or R
Cross-functional and senior-leadership influence - you raise others' experiment quality without direct authority

Nice to Have:

Familiarity with a modern experimentation platform such as Statsig
Experience building an experimentation practice or culture from the ground up
Background in B2B SaaS, CRM, or product-led growth, and familiarity with the measurement challenges these motions create
Multi-tenant or marketplace product experience (agency → sub-account → end-customer structures)