Staff Data Scientist - Experimentation & Causal Inference
HighLevelAbout the role
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:
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
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