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Internship Causal Inference Jobs in California (NOW HIRING)

Intuit

Staff AI Research Scientist

Mountain View, CA

$209K - $283K/yr

... causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI ... and interns through goal setting and technical directions that lead to positive outcomes ...

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Intuit

Staff AI Research Scientist

Mountain View, CA · On-site

$209K - $283K/yr

... causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI ... Mentoring junior researchers, AI scientists, and interns through goal setting and technical ...

Intuit

Staff AI Research Scientist

Mountain View, CA · On-site

$209K - $283K/yr

... causal inference, simulation-based planning, agentic and multi-agent systems, neuro-symbolic AI ... and interns through goal setting and technical directions that lead to positive outcomes ...

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Internship Causal Inference information

What types of projects and team collaborations can I expect during an internship in causal inference?

As an intern in Causal Inference, you will typically work on projects focused on analyzing data to determine cause-and-effect relationships, such as assessing the impact of interventions or policy changes. You may collaborate with data scientists, statisticians, and domain experts, contributing to experimental design, data cleaning, and the application of statistical methods. Interns often participate in weekly team meetings, present findings, and receive mentorship from senior researchers. This hands-on experience provides valuable exposure to both technical skills and interdisciplinary teamwork, which are crucial for growth in quantitative research roles.

What are the key skills and qualifications needed to thrive as an internship in causal inference, and why are they important?

To thrive in an Internship Causal Inference role, you need a solid background in statistics, econometrics, and data analysis, typically supported by coursework or degrees in statistics, economics, or related quantitative fields. Familiarity with statistical programming languages such as R or Python, and experience with causal inference frameworks and tools like propensity score matching or regression discontinuity, are commonly required. Strong problem-solving abilities, attention to detail, and effective communication skills help interns interpret results and collaborate with research teams. These skills and qualities are essential to ensure rigorous and meaningful analysis that informs data-driven decisions.

What is the difference between Internship Causal Inference vs Data Analyst?

AspectInternship Causal InferenceData Analyst
Required CredentialsUndergraduate or graduate in statistics, economics, or related fieldsDegree in statistics, data science, or related fields
Work EnvironmentResearch-focused, often in academia or research institutionsBusiness, corporate, or consulting settings
Employer & Industry UsageUniversities, research labs, tech companiesFinance, marketing, healthcare, tech companies
Comparison Search IntentUnderstanding causal inference techniques during internshipsAnalyzing data to inform business decisions

Internship Causal Inference roles focus on applying statistical methods to identify cause-effect relationships, often in research settings. Data Analyst roles involve interpreting data to support business strategies. While both require analytical skills, causal inference internships emphasize research and advanced statistical techniques, whereas data analyst positions focus on data processing and reporting.

What is an internship in causal inference?

An Internship in Causal Inference is a temporary position, typically for students or early-career professionals, that focuses on learning and applying methods to determine cause-and-effect relationships in data. Interns in this field work with statistical models, experimental designs, and software tools to analyze data and infer causal relationships, often in fields like economics, public health, or data science. These internships provide hands-on experience with real-world datasets, mentorship from experienced researchers, and opportunities to contribute to ongoing projects. Participants gain valuable skills in programming, statistical analysis, and research methodology, which are highly sought after in both academia and industry.

What are the most commonly searched types of Causal Inference jobs in California?

The most popular types of Causal Inference jobs in California are:

What are popular job titles related to Internship Causal Inference jobs in California?

For Internship Causal Inference jobs in California, the most frequently searched job titles are:

What job categories do people searching Internship Causal Inference jobs in California look for?

The top searched job categories for Internship Causal Inference jobs in California are:

What cities in California are hiring for Internship Causal Inference jobs?

Cities in California with the most Internship Causal Inference job openings:

Internship Causal Inference jobs near you
Infographic showing various Internship Causal Inference job openings in California as of August 2026, with employment types broken down into 10% Internship, 65% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Business and Marketing Data Scientist

Socket.dev

Mountain View, CA • On-site

$177.55 - $198/hr

Other

Posted 11 days ago

Job description

MINIMUM QUALIFICATIONS:

  • PhD degree in Economics, Statistics, Biostatistics or a related field, and experience in the job offered or in a Business and Marketing Data Scientist-related occupation.

  • Position requires experience in the following: Causal inference; Bayesian statistics; Machine Learning; R or Python; and SQL.


ABOUT THE JOB:

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.


As a Business Data Scientist on the YouTube Business Go-To-Market Impact Measurement team, you will work closely with business leaders to help shape the future of YouTube. You will leverage rigorous techniques from causal inference, advanced statistical modeling, and machine learning. It will be your responsibility to determine the best approach for solving problems and to communicate clearly with decision-makers who may or may not have a strong technical background.


We are looking for a detail-oriented problem solver with broad knowledge of causal inference, Bayesian statistics, and machine learning. We use a large set of methodologies, ranging from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. The ideal candidate is comfortable wearing multiple hats and is passionate about conducting causal studies and helping stakeholders implement data-driven decisions. Creative problem-solving and stakeholder management skills are critical.


Given the nature of the position, someone who loves to learn new things will be successful. Effective data scientists on our team keep up with advances in the causal inference literature. When existing methodologies are not well suited for the problem at hand, you will be encouraged to develop new methods in collaboration with your teammates and work with our summer interns on research projects. We also attend relevant conferences and organize internal events to keep our toolkit updated and to educate the broader Google community.


The US base salary range for this full-time position is $177,550 - $198,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google https://www.google.com/about/careers/applications/benefits/.


Position reports to the Google Mountain View, CA office & may allow for a hybrid schedule as per Google policy.


RESPONSIBILITIES:

  • Design and execute causal studies to address critical business questions.

  • Leverage advanced statistical models to find business insights in experimental and observational data.

  • Present and communicate actionable insights and recommendations to executives and cross-functional partners.

  • Serve as a peer reviewer and consultant for causal studies across the organization.

  • Stay current with the latest advancements in causal inference.

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