ZipRecruiter

Log In

1

Internship Causal Inference Jobs in Berkeley, CA

Wand

Product Data Analyst

San Francisco, CA · Remote

$145K - $175K/yr

... internships) * SQL fluency -- complex joins, incremental computation, window functions, query ... Causal inference methods (diff-in-diff, regression discontinuity, propensity matching) * Prior work ...

All JobsCausal Inference JobsInternship Causal Inference JobsInternship Causal Inference Jobs in Berkeley, CA

Internship Causal Inference information

See Berkeley, CA salary details

$11

$21

$29

How much do internship causal inference jobs pay per hour?

As of Aug 2, 2026, the average hourly pay for internship causal inference in Berkeley, CA is $21.19, according to ZipRecruiter salary data. Most workers in this role earn between $17.64 and $23.56 per hour, depending on experience, location, and employer.

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 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 popular job titles related to Internship Causal Inference jobs in Berkeley, CA? For Internship Causal Inference jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Internship Causal Inference jobs in Berkeley, CA look for? The top searched job categories for Internship Causal Inference jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Internship Causal Inference jobs? Cities near Berkeley, CA with the most Internship Causal Inference job openings:
Internship Causal Inference jobs near you
Infographic showing various Internship Causal Inference job openings in Berkeley, CA as of July 2026, with employment types broken down into 85% Full Time, 13% Part Time, 1% Contract, and 1% Summer. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $44,073 per year, or $21.2 per hour.

Data Scientist, Core Data - PhD (2026)

Figma

San Francisco, CA • On-site, Remote

Other

Re-posted yesterday

Job description

We're looking for a research-minded Data Scientist to join the Core Data team. This team is a group of analytics professionals and Engineers building the foundational platforms for data science at Figma.  We build the experimentation, analytics, and AI tooling that every product team relies on to make confident, data-driven decisions, partnering closely with Data Infra, ML, and Applied Science to evolve our platforms and embed AI into the daily workflows of data scientists across the company.

This role is for someone who thrives at the intersection of rigorous research and real-world impact. You'll bring PhD-level depth to problems that matter. This includes advancing our experimentation platform and developing machine learning-based analytical systems. You will also help craft how we measure AI-powered features through causal inference and statistical modeling.  

This is a full time role that can be held from one of our US hubs or remotely in the United States. 

What you'll do at Figma:

  • Partner across teams to define and track important metrics, develop experiments, and uncover insights that inform strategic decisions  
  • Accelerate Figma's experimentation platform and methodology, including A/B testing frameworks and causal inference techniques
  • Construct models and analytical frameworks based on machine learning to support product, platform, and business initiatives  
  • Create tools, datasets, and systems that enable others to work with data more efficiently and rigorously
  • Complete and own complex data projects end-to-end, from problem prioritisation to solution delivery  
  • Drive data quality, accessibility, and the democratization of data across the organization

We'd love to hear from you if you have:

  • PhD in a quantitative field (Statistics, Computer Science, Economics, Operations Research, Physics, or related) with a strong foundation in statistical methods, experimentation, and/or machine learning
  • Fluency in SQL and proficiency in a scripting language like Python or R, with exposure to distributed data systems (e.g. Snowflake) through research or internships
  • Ability to communicate technical concepts clearly to both technical and non-technical audiences
  • A curious and rigorous mindset, with a passion for translating research into real-world impact

While it's not required, it's an added plus if you also have:

  • Publications or research experience in experimentation or applied ML; industry internship experience applying data science to product or business problems
  • An AI-native mindset, with exposure to or interest in LLM analytics, AI product measurement, or evaluating the impact of AI-powered features
  • A self-starter attitude and the ability to thrive in ambiguous and fast-paced environments
At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Apply