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Causal Inference Jobs in California (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 ...

Apple

Applied ML Science Manager

Cupertino, CA

$198.30K - $342.80K/yr

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference solutions, that directly impact our products and provide a granular understanding of key business ...

Apple

Applied ML Science Manager

Cupertino, CA · On-site

We are currently seeking an experienced and passionate Applied ML Science Manager to lead a dynamic team, whose goal is to provide innovative products at the intersection of causal inference ...

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Causal Inference information

See California salary details

$54.3K

$97.9K

$133.7K

How much do causal inference jobs pay per year?

As of May 29, 2026, the average yearly pay for causal inference in California is $97,931.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,900.00 and $107,100.00 per year, depending on experience, location, and employer.

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.

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.
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What are the most commonly searched types of Causal Inference jobs in California? The most popular types of Causal Inference jobs in California are:
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What job categories do people searching Causal Inference jobs in California look for? The top searched job categories for Causal Inference jobs in California are:
What cities in California are hiring for Causal Inference jobs? Cities in California with the most Causal Inference job openings:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in California as of May 2026, with employment types broken down into 1% Internship, 95% Full Time, 3% Part Time, and 1% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $97,931 per year, or $47.1 per hour.
Causal Impact Scientist III

Causal Impact Scientist III

L.A. Care Health Plan

Los Angeles, CA

$175.68K - $216.22K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 14 days ago

L.A. Care Health Plan rating

9.1

Company rating: 9.1 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

24th of 259 rated insurance

Job description

Salary Range:  $135,136.00 (Min.) - $175,676.00 (Mid.) - $216,218.00 (Max.)

Established in 1997, L.A. Care Health Plan is an independent public agency created by the state of California to provide health coverage to low-income Los Angeles County residents. We are the nation's largest publicly operated health plan. Serving more than 2 million members, we make sure our members get the right care at the right place at the right time.
Mission: L.A. Care's mission is to provide access to quality health care for Los Angeles County's vulnerable and low-income communities and residents and to support the safety net required to achieve that purpose.
 

Job Summary

The Causal Impact Scientist III is a senior, hands-on practitioner responsible for developing and implementing scalable approaches to measure program impact across the organization. This position not only evaluates the historical effectiveness of interventions but also informs future program design, equity strategies, sub-population targeting, and policy decisions.

The Causal Impact Scientist III leads the creation of modular, reusable analytic pipelines and causal inference frameworks that can be applied across multiple programs and initiatives. This position sets methodological standards, ensures analytic reproducibility, and mentors staff in best practices for causal analysis, study design, and evidence communication.

The Causal Impact Scientist III collaborates with cross-functional teams integrating predictive and causal insights to drive enterprise-wide decisions. Acts as a Subject Matter Expert (SME), serves as a resource and mentor for other staff.  

Duties
Lead the design, implementation, and interpretation of advanced causal inference studies across multiple health plan programs using techniques such as difference-in-differences, propensity score methods, inverse probability weighting, synthetic controls, interrupted time series, and Randomized Control Trials (RCTs).   Develop scalable, modular frameworks for impact evaluation that can be ported across programs and interventions, ensuring reproducibility, transparency, and efficient deployment.   Identify which interventions deliver the greatest value, equity, and member outcomes, guiding sub-population targeting, program improvement, and policy recommendations.   Mentor and provide technical guidance to staff on methodology, coding, documentation, and analytic rigor.   Build and maintain robust, reproducible analytical pipelines in Python (PySpark, pandas) and R; leverage Snowflake/Snowpark, Spark, and cloud-based computing resources to scale analyses.   Lead GitHub version control, peer review, and code standardization practices to ensure high-quality, reusable analytic assets.   Manage project deliverables and team workflows in Jira; document methods, assumptions, and results clearly in Confluence.   Translate complex analytic findings into actionable insights, guiding executive decision-making and influencing strategic initiatives.   Collaborate with cross-functional teams to integrate causal and predictive insights for comprehensive program evaluation.   Stay current on emerging methods in causal inference, impact evaluation, and population health analytics, incorporating new approaches into the team's analytic toolkit.   Apply subject matter expertise in evaluating business operations and processes. Identify areas where technical solutions would improve business performance. Consult across business operations, provide mentorship, and contribute specialized knowledge. Ensure that the facts and details are correct so that the program's deliverable meets the needs of the department, organization and legislation's policies, standards, and best practices. Provide training and recommend process improvements as needed.   Perform other duties as assigned
Duties Continued
Education Required
Master's Degree
In lieu of degree, equivalent education and/or experience may be considered.
Education Preferred
Doctorate Degree
Experience

Required:

At least 6 years of professional experience conducting causal inference or program evaluation analyses in healthcare, public health, or a related field.

Proven experience applying advanced causal inference methods (e.g., difference-in-differences, matching, synthetic controls, RCTs) to observational and experimental healthcare data.

Hands-on experience developing and deploying scalable analytic pipelines in Python and/or R.

Experience collaborating with cross-functional teams, translating analytic findings into actionable recommendations.

Experience working with large-scale datasets in Spark or distributed computing environments.

Preferred:
Extensive experience in Managed Care Plans (Medicaid, Medicare, ACA Exchange), including understanding claims, encounters, eligibility, provider networks, and quality metrics.

Experience designing and implementing (RCTs) or complex quasi-experimental studies in healthcare operational settings.

Experience applying causal inference methods to program targeting, equity assessment, and policy evaluation.

Experience building reusable analytic tools and frameworks that can be applied across multiple use cases.

Skills

Required:
Expertise in causal inference methods: difference-in-differences, propensity score matching, synthetic controls, interrupted time series, RCT design, and related techniques.

Advanced programming skills in Python (pandas, PySpark, statsmodels) and R (dplyr, ggplot2, MatchIt, did, or similar).

Ability to design reproducible, modular, and scalable analytic pipelines.

Proficiency with GitHub, Confluence, and Jira for code collaboration, documentation, and project management.

Strong written and verbal communication skills; ability to translate complex analyses into actionable insights for stakeholders.

Strong collaboration skills and experience mentoring junior team members.

Ability to manage multiple projects and priorities in a fast-paced environment.

Ability to apply critical thinking skills in causal reasoning to address complex healthcare data problems.
 

Demonstrated ability to mentor and guide staff on methodology, coding, and documentation.

Strong communication skills, including documenting methodology, assumptions, and results for both technical and non-technical stakeholders.

Preferred:

Working knowledge of modern cloud-based analytic ecosystems (Snowflake, Azure).

Knowledge of visualization and reporting frameworks to communicate impact results to leadership.

Knowledge of Snowflake/Snowpark, cloud computing, and distributed data platforms.

Strong presentation skills, with experience briefing senior leadership on analytic findings and strategic implications.

Knowledge of health equity analytics and population segmentation for targeting interventions.

Licenses/Certifications Required
Licenses/Certifications Preferred
Certified Health Data Analyst (CHDA)
Certified Analytics Professional (CAP)
Snowflake SnowPro Core Certification
SnowPro Specialty: Data Engineering or Snowpark
Health Economics and Outcomes Research (HEOR) Certification or Graduate Certificate
Causal Inference for Data Science (e.g., University of Pennsylvania or MITx MicroMasters)
Required Training
Physical Requirements
Light
Additional Information

Salary Range Disclaimer: The expected pay range is based on many factors such as geography, experience, education, and the market.  The range is subject to change.

L.A. Care offers a wide range of benefits including

  • Paid Time Off (PTO)
  • Tuition Reimbursement
  • Retirement Plans
  • Medical, Dental and Vision
  • Wellness Program
  • Volunteer Time Off (VTO)

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