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Remote Causal Inference Jobs (NOW HIRING)

Airbnb

Data Scientist, Platform

As a Data Scientist working on Causal Inference in CS, you will have the opportunity to collaborate ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

Airbnb

Data Scientist, Platform

As a Data Scientist working on Causal Inference in CS, you will have the opportunity to collaborate ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

Airbnb

Senior Data Scientist, Trust (Inference)

Deep expertise in experimentation and causal inference. * Strong knowledge of Bayesian modeling and ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

Mondo

Lead Decision Science Analyst

Oak Brook, IL · Remote

$80 - $90/hr

Lead Decision Science Analyst, Remote (EST/ CST hours). Start date is ASAP for this 6-Month ... Design, analyze, and interpret A/B tests and other causal inference approaches, ensuring test ...

Airbnb

Senior Data Scientist, Trust (Inference)

Deep expertise in experimentation and causal inference. * Strong knowledge of Bayesian modeling and ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

ORAU

Senior Advisor - Payment Model Evaluation

$89 - $95/hr

Remote Salary: $89.00 - $95.00 per hour ORAU is seeking a fully remote Senior Advisor - Payment ... Skills and Competencies: * Strong command of claims data analysis, causal inference methods ...

Reddit

Staff Data Scientist, Marketing

We're completely remote friendly. Team Description: The Marketing Science team at Reddit leverages ... We serve as the engine behind our growth, using advanced experimentation, causal inference, and ...

Enterprise Holdings

Data Scientist

Saint Louis, MO · Remote

Team members who choose virtual / remote work should have an adequate space to serve as their home ... Apply causal inference techniques using observational data to uncover relationships * Prepare and ...

Gremlin

Data Scientist, AI/ML

$220K - $290K/yr

... scale causal inference in production * Experience standing up MLOps tooling such as model serving, monitoring, or feature store infrastructure * Working in Remote first environments * Has been ...

Gremlin

Data Scientist, AI/ML

$220K - $290K/yr

... scale causal inference in production * Experience standing up MLOps tooling such as model serving, monitoring, or feature store infrastructure * Working in Remote first environments * Has been ...

Walmart

Staff, Data Scientist (Pricing)

Bentonville, AR · On-site +1

This is a full-time onsite role based in Bentonville, AR or Sunnyvale, CA; remote and hybrid ... Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches ...

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How much do remote causal inference jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for remote causal inference in the United States is $56.81, according to ZipRecruiter salary data. Most workers in this role earn between $46.63 and $67.31 per hour, depending on experience, location, and employer.

What is a remote causal inference?

A Remote Causal Inference job involves using statistical and analytical methods to determine cause-and-effect relationships from data, often for fields like healthcare, social sciences, or business. Professionals in this role work remotely, leveraging tools such as R, Python, or specialized software to analyze experiments, observational studies, or large datasets. Their insights help organizations make data-driven decisions, design better interventions, and accurately measure the impact of policies or treatments. Strong skills in statistics, machine learning, and communication are essential for success in this position.

What are the key skills and qualifications needed to thrive as a remote causal inference specialist?

To thrive as a Remote Causal Inference Specialist, you need strong quantitative and statistical skills, a solid background in econometrics or data science, and typically an advanced degree in a related field. Proficiency with statistical programming languages such as R or Python, experience with causal inference frameworks like propensity score matching or instrumental variables, and familiarity with data visualization tools are crucial. Outstanding problem-solving abilities, clear communication, and self-motivation are essential soft skills for working independently and conveying complex results to non-technical stakeholders. These skills enable accurate, actionable insights from data, which drive evidence-based decision-making in remote, collaborative environments.

How does a remote causal inference specialist typically collaborate with cross-functional teams, and what tools are commonly used?

As a remote Causal Inference specialist, you’ll frequently work with data scientists, product managers, and engineers to design and interpret experiments, analyze observational data, and provide actionable insights. Collaboration usually happens through regular video meetings, shared documentation, and project management tools. Commonly used platforms include Slack or Microsoft Teams for communication, GitHub for code collaboration, and Jupyter Notebooks or RMarkdown for sharing reproducible analyses. These tools help ensure transparency and maintain strong teamwork despite the remote environment.
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Remote Causal Inference jobs near you
Infographic showing various Remote Causal Inference job openings in the United States as of August 2026, with employment types broken down into 84% Full Time, 15% Part Time, and 1% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $118,171 per year, or $56.8 per hour.

Staff Data Scientist - Experimentation & Causal Inference

HighLevel

Remote

$163K - $220K/yr

Full-time

Re-posted 18 days ago

Job description

About HighLevel:
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 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)

$163,400 - $220,000 a year
EEO Statement:
The company is an Equal Opportunity Employer. As an employer subject to affirmative action regulations, we invite you to voluntarily provide the following demographic information. This information is used solely for compliance with government recordkeeping, reporting, and other legal requirements. Providing this information is voluntary and refusal to do so will not affect your application status. This data will be kept separate from your application and will not be used in the hiring decision.
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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