ZipRecruiter

Log In

1

Causal Inference Jobs (NOW HIRING)

Alembic

Research Engineer - Causal AI

San Francisco, CA · On-site

$200K - $250K/yr

Build production systems for causal inference that maintain statistical rigor at enterprise scale * Develop algorithms that are both mathematically sound and computationally efficient * Collaborate ...

YO AI Labs

Data Scientist

Glendale, CA

Robust knowledge of causal inference approaches such as propensity scores, synthetic controls, difference-in-differences, doubly robust methods, meta learners, and uplift modeling. * Expertise in A/B ...

YO AI Labs

Data Scientist

Glendale, CA · On-site

Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, causal inference (difference-in-differences, propensity scores, instrumental ...

Roku

Senior Research Data Scientist

Boston, NY · On-site

$330K - $375K/yr

About the role As a Senior Research Data Scientist on Roku's Data Science team, you will lead the development of a best-in-class causal inference platform that measures and optimizes the true ...

next page

Showing results 1-20

All JobsCausal Inference Jobs

Causal Inference information

See salary details

$55K

$99.2K

$135.5K

How much do causal inference jobs pay per year?

As of Jul 10, 2026, the average yearly pay for causal inference in the United States is $99,231.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $108,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

More about Causal Inference jobs
What cities are hiring for Causal Inference jobs? Cities with the most Causal Inference job openings:
What are the most commonly searched types of Causal Inference jobs? The most popular types of Causal Inference jobs are:
What states have the most Causal Inference jobs? States with the most job openings for Causal Inference jobs include:
What job categories do people searching Causal Inference jobs look for? The top searched job categories for Causal Inference jobs are:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in the United States as of July 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $99,231 per year, or $47.7 per hour.
Machine Learning Engineer, Causal Inference, Level 5

Machine Learning Engineer, Causal Inference, Level 5

Snap, Inc.

San Francisco, CA

Full-time

Medical

Posted 10 days ago

Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.


The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

We're looking for a Machine Learning Engineer to join Snap Inc!

What you'll do:

  • Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business

  • Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data

  • Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies

  • Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability

  • Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure

  • Contribute to rapid iteration cycles while ensuring methodological rigor

Knowledge, Skills & Abilities:

  • Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)

  • Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure

  • Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalM etc.)

  • Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism

  • Comfortable working independently and collaborating across cross-functional teams

  • Strong communication and mentorship skills; able to translate technical insights for non-technical partners

Minimum Qualifications:

  • Bachelor's degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience

  • 5+ years of post-Bachelor's experience in machine learning, with hands-on experience in causal inference or experimentation; or Master's degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience

  • Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques

  • Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems

Preferred Qualifications:

  • Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research

  • Experience with causal inference libraries such as CausalML, EconML or DoWhy

  • Background in deploying models in production settings and working with ML or experimentation infrastructure

  • Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty

  • Experience applying causal inference in domains like personalization, ad or marketplace dynamics

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $209,000-$313,000 annually.


Zone B:

The base salary range for this position is $199,000-$297,000 annually.

Zone C:

The base salary range for this position is $178,000-$266,000 annually.This position is eligible for equity in the form of RSUs.

Apply