ZipRecruiter

Log In

1

Causal Inference Jobs (NOW HIRING)

previous pagenext page

Showing results 21-40

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 Aug 20, 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 is a causal inference?

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.

What skills and qualifications are needed for a causal inference position?

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 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.

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 August 2026, with employment types broken down into 81% Full Time, 18% Part Time, and 1% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $99,231 per year, or $47.7 per hour.

Senior Data Scientist, Causal Inference

Socotra, Inc.

San Francisco, CA • On-site

$148 - $185/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 15 days ago

Job description

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

The Growth Products team drives rider and driver acquisition to scale the business and balance the marketplace. We specialize in incentive and messaging targeting, budget optimization, and paid media measurement, and move rapidly to test new ideas and products.

As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels.

Responsibilities:
  • Deliver results across the entire lifecycle of data science solutions for Growth: from defining the problem with cross‑functional stakeholders to deploying production models that address key business problems.
  • Own complex domains and develop long‑term roadmaps to maximize business impact.
  • Build statistical pipelines, write production code, and design/analyze experiments.
  • Participate in the science on‑call rotation to ensure automated campaigns operate successfully.
Experience:
  • Advanced degree in statistics, economics, mathematics, or equivalent industry experience.
  • 4+ years of industry experience in causal inference or data science.
  • Proven ability to apply statistics to unstructured problems and deliver measurable results.
  • Deep technical expertise in causal inference and tackling challenging measurement problems.
  • Expertise in marketing mix modeling is highly preferred.
  • Expertise in SQL and experience with large‑scale data platforms.
  • Proficiency in Python and working within production coding environments.
Benefits:
  • Great medical, dental, and vision insurance options with additional programs available when enrolled.
  • Mental health benefits.
  • Family building benefits.
  • Child care and pet benefits.
  • 401(k) plan with company match to help save for your future.
  • In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off.
  • 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible.
  • Subsidized commuter benefits.
  • Monthly Lyft credits and complimentary Lyft Pink membership.

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in‑office to foster a collaborative work environment and company culture. This role will be in‑office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in‑office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

#J-18808-Ljbffr

Apply