Staff Data Scientist – Experimentation, Causal Inference

HighLevel

Staff Data Scientist responsible for experimentation and causal inference, shaping product decisions at HighLevel. Leading statistical methodologies in a fast-paced AI-driven SaaS environment.

Posted 7/20/2026full-timeRemote • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and analyzing online controlled experiments, applying rigorous statistical methods, and influencing cross-functional teams to enhance experiment quality. Proficient in causal inference and statistical analysis within fast-paced, multi-product environments.

Highest-signal resume keywords

Applied StatisticsCausal InferenceExperiment DesignSQL ProficiencyPython or R Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ApproachHypothesis TestingVariance ReductionFrequentist FoundationsBayesian MethodsPower AnalysisExperiment AnalysisCausal Inference TechniquesData AnalysisExperimentation Methodology

Soft Skills

Cross-Functional InfluenceLeadership InfluenceCommunication Skills

Industry Keywords

Online Controlled ExperimentsSmall-Sample EnvironmentsMulti-Product ContextsDecision-Grade GuidanceExperimentation Curriculum

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

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