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Internship Causal Inference Jobs (NOW HIRING)

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

Senior ML/Data Engineer

New York, NY · On-site

$116K - $157K/yr

... internships, university placements, coursework, and academic projects. * Proven experience ... Experience with causal inference, counterfactual modelling, or simulation. * Experience working ...

Senior ML/Data Engineer

New York, NY · On-site

$116K - $157K/yr

... internships, university placements, coursework, and academic projects. * Proven experience ... Experience with causal inference, counterfactual modelling, or simulation. * Experience working ...

Senior ML/Data Engineer

New York, NY

$116K - $157K/yr

... internships, university placements, coursework, and academic projects. * Proven experience ... Experience with causal inference, counterfactual modelling, or simulation. * Experience working ...

We're now looking for AI, NLP, Machine Learning, Data Science, and Math PhD Interns to work ... causal inference, and other related disciplines * Programming skills and familiarity of modern ML ...

We're now looking for AI, NLP, Machine Learning, Data Science, and Math PhD Interns to work ... causal inference, and other related disciplines * Programming skills and familiarity of modern ML ...

Product Analyst

Chicago, IL · 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 ...

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Internship Causal Inference information

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How much do internship causal inference jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for internship causal inference in the United States is $17.31, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

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 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.

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What cities are hiring for Internship Causal Inference jobs?

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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 Internship Causal Inference jobs?

States with the most job openings for Internship Causal Inference jobs include:

Infographic showing various Internship Causal Inference job openings in the United States as of August 2026, with employment types broken down into 10% Internship, 56% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $35,995 per year, or $17.3 per hour.

Full-time

Re-posted 20 days ago


Job description

Job Summary:
Wand is a company that makes gaming magical by building tools that enhance player experiences across thousands of PC games. They are seeking a Product Data Analyst to own key metrics, conduct deep investigations, and design experiments that drive product decisions and improvements.
Responsibilities:
• You'll own the dashboards and definitions Product, Growth, Marketing, and Finance rely on every day.
• When a number moves, you'll know before anyone else — and you'll know why.
• You'll build in Hex, document in our spec, and enforce consistent definitions across teams.
• When retention dips or a funnel leaks, you won't stop at the symptom.
• You'll follow the thread — through cohorts, segments, the quirks of specific games — until you can name the cause and recommend a fix.
• You'll notice when a chart doesn't look right and you won't leave it alone.
• You'll design and analyze A/B tests on product changes, pricing, onboarding, and growth initiatives.
• You'll set sample sizes, call stat sig, and write the readouts.
• When a result is noisy or a test is underpowered, you'll say so — even when the PM is ready to ship.
• You'll work directly with our leaders on the questions that don't have obvious answers.
• You'll frame the question, run the analysis, and deliver the recommendation.
Qualifications:
Required:
• 5 years in a Data Analyst, Product Analyst, or Business Intelligence role (excluding internships)
• SQL fluency — complex joins, incremental computation, window functions, query optimization; comfortable with large, messy datasets
• Python for analysis — pandas and statistical libraries; you don't freeze when the answer requires code
• Experimentation chops — hypothesis testing, sample sizing, and the judgment to tell a p-value from an insight
• A track record of moving metrics you cared about (we'll ask you to walk us through one)
• Direct communication — you can tell a leader an uncomfortable answer without burying it in caveats
Preferred:
• Degree in a quantitative field
• Hex, dbt, and Bigquery experience
• Prior growth-stage consumer software or gaming experience
• Causal inference methods (diff-in-diff, regression discontinuity, propensity matching)
• Prior work on LTV, retention, or subscription models
• Experience building metrics frameworks or KPI hierarchies from scratch
Company:
Wand makes gaming magical. Founded in 2018, the company is headquartered in Miami, USA, with a team of 11-50 employees. The company is currently Early Stage.