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Staff Data Scientist - Experimentation & Causal Inference

3 weeks, 1 day ago
United States
Full-time
Lead
Data Scientist
Data Science and Analytics
CRM Machine Learning Python R SQL
Apply Now
HighLevel

HighLevel

HighLevel provides an all-in-one sales and marketing platform that agencies can white label and resell, offering tools and resources designed to help businesses consolidate their marketing efforts and achieve their growth objectives.

Internet Software & Services
251-1K
Founded 2018
$60M raised
View All Jobs 72

Description

  • Define the end-to-end experimentation methodology from hypothesis through decision and make it the default across teams.
  • Own the statistical approach for small-sample experimentation, including significance testing, multiple comparisons, sequential testing, and variance reduction.
  • Develop methods for analyzing clustered and hierarchical data across user, sub-account/location, and agency levels.
  • Apply causal inference techniques when controlled experiments are not feasible, especially for churn, onboarding, and go-to-market questions.
  • Design guardrails for running multiple concurrent experiments, including layering, orthogonal tests, and holdouts.
  • Partner with AI/ML teams to design and evaluate experiments for AI features and non-deterministic systems.
  • Run the experiment review forum and determine what qualifies as a valid result.
  • Build experimentation curricula, templates, and standards to improve PM and analyst capability.
  • Partner with Analytics Engineering to ensure governed, experiment-ready data and consistent metric definitions.
  • Influence leadership and cross-functional partners by translating statistical findings into clear recommendations.

Requirements

  • 9+ years of experience in data science, product analytics, or applied statistics.
  • Deep hands-on experience designing and analyzing online controlled experiments at scale.
  • Strong applied statistics knowledge, including frequentist and Bayesian methods, power analysis, variance reduction, and A/B testing failure modes.
  • Practical causal inference experience and sound judgment about causal versus non-causal results.
  • Experience in small-sample, fast-paced, multi-product environments.
  • Strong SQL skills and working proficiency in Python or R.
  • Experience influencing cross-functional teams and senior leadership without direct authority.
  • Familiarity with a modern experimentation platform such as Statsig (preferred).
  • Experience building an experimentation practice or culture from the ground up (preferred).
  • Background in B2B SaaS, CRM, or product-led growth, with familiarity in related measurement challenges (preferred).
  • Multi-tenant or marketplace product experience, such as agency to sub-account to end-customer structures (preferred).

Interested in this position?

Apply directly on the company website

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