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Internship Causal Inference Jobs (NOW HIRING)

Causal inference, causal discovery, double ML, policy learning, dynamic panel/choice modeling ... About the Internship Program At Netflix, we offer a personalized experience for interns, and our ...

... 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 ...

... 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 ...

... 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

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How much do internship causal inference jobs pay per hour?

As of Sep 12, 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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Infographic showing various Internship Causal Inference job openings in the United States as of September 2026, with employment types broken down into 14% Internship, 1% As Needed, 68% Full Time, 16% Part Time, 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.

Software Development Engineer, Marketing Measurement and Performance Science

Seattle, WA • On-site

Amazon
IT Services • 10K+ employees

$159K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago


Amazon rating

7.4

Company rating: 7.4 out of 10

Based on 7,173 frontline employees who took The Breakroom Quiz


Job description

Marketing Measurement and Performance Science (MAPS) measures the incremental impact of Amazon's marketing investments on customer perception, action, and purchases - covering billions in annual fixed marketing (FM) spend across the full funnel. Our outputs provide spend insights and recommendations that power total investment decisions at the S-team level for OP planning, BU-level budget allocation, and in-year spend guidance.
As an SDE II, you'll design and build the systems that transform and harmonize data from 20+ external partners - each with their own schemas, grains, cadences, and nuances - into the measurement-grade inputs that power COSMOS, Amazon's FM causal measurement framework. The problems you will coverage go beyond standard ETL responsibilities - you'll build AI-native systems that reconcile providers with incompatible definitions, handle split metric ownership, manage retroactive revisions, and maintain the immutable snapshots and deterministic joins that causal inference demands.
Key job responsibilities
- Design, develop, and maintain measurement-grade data systems at scale - ingesting, standardizing, and vending marketing data from 20+ external and internal sources that feed causal modeling and MLOps systems.
- Own full lifecycle delivery of production software on complex, ambiguous problems - from design through launch and ongoing operations - with independence and minimal guidance.
- Build automated validation pipelines and data quality frameworks that enforce contracts, detect anomalies across providers, and ensure measurement-grade integrity at every stage.
- Define statistical methods for outlier detection, diagnose root causes systematically, and determine corrective actions to maintain data trust.
- Partner across science, product, and engineering teams to scope solutions, navigate constraints, and ship the most efficient path from prototype to production.
- Write clean, well-tested code (Python, Scala, or Java) and mentor junior engineers on system design, code quality, and operational best practices.
A day in the life
Day to day, you'll build and scale multi-layer automated validation pipelines, with clear data lineage so every model run is fully reproducible. Our vision is to scale our infrastructure across new business units and geographies reaching 90%+ coverage of Amazon's FM spend, and develop self-service catalog and observability tooling that lets scientists and partner teams explore our data without filing tickets. You'll also have a direct influence on schema governance - designing systems that enforce data standards at the point of contract, detect drift from providers, and keep our specifications current as partnerships expand.
You'll collaborate closely with causal scientists, economists, product managers, and agency data ops teams - translating measurement requirements into scalable technical solutions. This is a high-ownership role where your work directly determines whether Amazon's leadership can trust the numbers behind billion-dollar marketing investment decisions.
About the team
Within MAPS, the Marketing Inputs & Data Automation (MIDA) team owns the measurement-grade data layer that sets the ceiling on what our causal models can measure, where they can operate, and how confident leadership should be in the outputs. We build and operate large-scale data infrastructure and data assets - ingesting, validating, harmonizing, and vending data from 20+ third-party providers (agencies, aggregators, publishers) across multiple Amazon business units and marketing channels, with global coverage. Our pipeline is purpose-built for the high bar of causal inference - not dashboards or reporting - requiring strict temporal integrity, historical stability, multi-layer validation, full lineage, and reproducibility at every stage.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually

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About Amazon

Sourced by ZipRecruiter

Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.

Industry

It services, book publishers, retail, real estate, computer and electronic product manufacturing and software development

Company size

10,000+ Employees

Headquarters location

Seattle, WA, US