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Causal Inference Economist Jobs (NOW HIRING)

Amazon

Economist, Ads Measurement Science

New York, NY · On-site

We are hiring an Economist on the team to develop the next generation of incrementality measurement ... Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally ...

Neura Market

... economics, computer science, etc.), or PhD in relevant fields. * Strong knowledge of causal inference and experimental design. * Strong knowledge of Bayesian modeling and statistical inference.

Neura Market

Economist

San Francisco, CA · On-site

Apply causal inference and/or structural modeling techniques to study AI-driven economic change. * Collaborate with cross-functional teams to translate research questions into testable frameworks and ...

Amazon

Economist, Ads Measurement Science

New York, NY · On-site

As an Economist on the team, you will lead the design, implementation, and validation of large ... Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally ...

OpenAI

Economist

San Francisco, CA · On-site

Apply causal inference and/or structural modeling techniques to study AI-driven economic change. * Collaborate with cross-functional teams to translate research questions into testable frameworks and ...

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 ... Background in econometrics, statistics, or computational social science * Experience in marketing ...

OpenAI

Economist

San Francisco, CA · On-site

$266K - $385K/yr

Apply causal inference and/or structural modeling techniques to study AI-driven economic change. * Collaborate with cross-functional teams to translate research questions into testable frameworks and ...

Instacart

Senior Machine Learning Engineer, Economist

OR · On-site +1

$91K - $116K/yr

Experience applying causal inference methodologies to both observational and experimental datasets ... A PhD in Economics or a closely related field with a focus on data-intense problems. * Intern ...

YO AI Labs

Data Scientist

Glendale, CA

Bachelors in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or a related field + 7 years of experience with an emphasis on experimentation or causal inference. * Strong ...

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Causal Inference Economist information

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How much do causal inference economist jobs pay per year?

As of Sep 10, 2026, the average yearly pay for causal inference economist in the United States is $82,064.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $98,500.00 per year, depending on experience, location, and employer.

What is a causal inference economist?

A Causal Inference Economist is a professional who specializes in using statistical and econometric methods to determine cause-and-effect relationships in economic data. Unlike traditional economists who might focus on correlations or trends, causal inference economists design studies and analyses that help identify whether one variable truly impacts another. They often use techniques like randomized controlled trials, natural experiments, and instrumental variables to isolate causal effects. Their work is crucial for informing policy decisions, evaluating interventions, and understanding the real-world impact of economic changes.

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

To thrive as a Causal Inference Economist, you need a strong background in econometrics, statistics, and microeconomic theory, often supported by a PhD in economics or a related quantitative field. Proficiency with statistical programming languages such as R, Python, or Stata, and familiarity with causal inference frameworks like difference-in-differences and instrumental variables, are typically required. Critical thinking, problem-solving, and the ability to communicate complex results clearly are standout soft skills in this role. These skills ensure rigorous analysis, actionable insights, and effective collaboration with both technical and non-technical stakeholders.

What are some common challenges faced by causal inference economists when working with real-world data?

Causal Inference Economists often encounter challenges such as dealing with incomplete or noisy data, identifying suitable natural experiments or instruments, and ensuring their assumptions for causal identification are valid. Real-world data rarely conforms perfectly to theoretical models, requiring creative problem-solving and strong statistical rigor. Collaboration with data engineers and domain experts is common to better understand the data’s limitations and context, which helps in designing robust empirical strategies.

What is the difference between Causal Inference Economist vs Data Scientist?

AspectCausal Inference EconomistData Scientist
Required CredentialsAdvanced degree in Economics, Statistics, or related fieldDegree in Computer Science, Statistics, or related field
Work EnvironmentResearch-focused, policy analysis, academia, or economic consultingTech companies, finance, healthcare, or startups
Employer & Industry UsageGovernment agencies, research institutions, economic consulting firmsTech firms, finance, e-commerce, and data-driven industries

While both roles analyze data, a Causal Inference Economist specializes in understanding cause-and-effect relationships within economic contexts, often using econometric methods. A Data Scientist has a broader focus on extracting insights from data across various domains, utilizing machine learning and statistical techniques. The roles overlap in data analysis skills but differ in their primary focus and industry applications.

Can causal inference be used in economics?

Causal inference is a key skill for a Causal Inference Economist, enabling the analysis of cause-and-effect relationships in economic data. It is widely used in economics to evaluate policies, understand market behaviors, and inform decision-making, often utilizing tools like randomized experiments, natural experiments, and statistical models. Proficiency in econometrics and statistical software is essential for applying causal inference methods effectively.

What jobs use causal inference?

Causal inference is used in roles such as economists, data scientists, policy analysts, and healthcare researchers to determine cause-and-effect relationships. These jobs often require skills in statistical analysis, programming, and familiarity with causal inference methods like randomized controlled trials and observational study techniques.

What are popular job titles related to Causal Inference Economist jobs?

For Causal Inference Economist jobs, the most frequently searched job titles are:

Infographic showing various Causal Inference Economist job openings in the United States as of September 2026, with employment types broken down into 6% Internship, 79% Full Time, 14% Part Time, and 1% Contract. Highlights an 65% Physical, 2% Hybrid, and 33% Remote job distribution, with an average salary of $82,064 per year, or $39.5 per hour.

Machine Learning Engineer, Causal Inference, Level 5

Los Angeles, CA

Snap, Inc.
See more jobs at Snap, Inc.external

Full-time

Medical

Re-posted 11 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

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.

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