Staff Data Scientist – Experimentation, Causal Inference

🔥 16 hours ago

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HighLevel

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

☁️ SaaS

🤝 B2B

💰 Series A on 2021-11

SaaS • Marketing • B2B

HighLevel is an all-in-one marketing and sales platform designed to help businesses grow and succeed. The platform consolidates various marketing tools into a single solution, providing features such as lead capture through landing pages, surveys, forms, and calendars, as well as tools for nurturing leads via automated messaging across multiple channels including phone, SMS, email, and social media. HighLevel offers customizable solutions like online appointment scheduling, multi-channel follow-up campaigns, and pipeline management. Additionally, businesses can build websites, funnels, and landing pages using the intuitive page builder. HighLevel supports integrating with existing systems via API, and offers a membership platform for community building and course management. The platform is targeted towards marketers and offers white-labeling options for businesses to brand the software as their own. With a community-driven development approach and award-winning support, HighLevel is focused on empowering businesses to streamline their operations and enhance their marketing efficiencies.

📋 Description

• 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.

🏖️ Benefits

• EEO Statement: The company is an Equal Opportunity Employer. • We invite you to voluntarily provide demographic information for compliance with government recordkeeping, reporting, and other legal requirements.