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Phd Causal Inference Jobs (NOW HIRING)

Engineer end‑to‑end scalable and robust Causal Inference products which provide Apple with an ... PhD in related field. * Hands‑on experience leveraging Generative AI to improve productivity and ...

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... PhD in related field Hands-on experience leveraging Generative AI to improve productivity and ...

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... PhD in related field Hands-on experience leveraging Generative AI to improve productivity and ...

Senior Research Data Scientist

Boston, MA · On-site

$330K - $375K/yr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

Sr. Research Data Scientist

Boston, MA · On-site

$330K - $375K/yr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

OR a PhD in Economics, Statistics, Computer Science, or related quantitative field * Proficiency in SQL * Proficiency with Python or R * Strong foundation in experimentation and causal inference ...

OR a PhD in Economics, Statistics, Computer Science, or related quantitative field * Proficiency in SQL * Proficiency with Python or R * Strong foundation in experimentation and causal inference ...

OR a PhD in Economics, Statistics, Computer Science, or related quantitative field * Proficiency in SQL * Proficiency with Python or R * Strong foundation in experimentation and causal inference ...

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Phd Causal Inference information

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How much do phd causal inference jobs pay per year?

As of Sep 10, 2026, the average yearly pay for phd causal inference in the United States is $122,928.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,000.00 and $138,000.00 per year, depending on experience, location, and employer.

What is a PhD in causal inference?

A PhD in Causal Inference is an advanced research degree focused on understanding and identifying cause-and-effect relationships using statistical and computational methods. Students in this field learn to design studies, analyze data, and develop new methodologies to answer complex causal questions in areas such as social sciences, medicine, economics, and artificial intelligence. Graduates often work in academia, research institutions, or industries where evidence-based decision-making is essential.

What are the key skills and qualifications needed to thrive as a PhD causal inference researcher?

To thrive as a PhD Causal Inference researcher, you need advanced knowledge of statistics, econometrics, and causal modeling, typically supported by a doctoral degree in a quantitative field. Familiarity with statistical programming languages (such as R or Python), specialized software (like STATA or SAS), and experience with experimental or quasi-experimental methods are essential. Strong analytical thinking, attention to detail, and the ability to communicate complex findings clearly make a candidate stand out. These skills ensure rigorous, credible research that can inform policy, product development, or scientific understanding by accurately identifying causal relationships.

What collaborative opportunities can a PhD specializing in causal inference expect within a multidisciplinary research team?

PhD professionals in Causal Inference frequently collaborate with experts from fields such as epidemiology, economics, computer science, and public health. They often work closely with data scientists, subject matter experts, and statisticians to design studies, interpret complex datasets, and develop robust analytical models. This multidisciplinary environment fosters continuous learning and often leads to co-authorship on research publications, participation in grant writing, and involvement in high-impact policy or product decisions. Effective communication and teamwork skills are essential to translate technical findings for diverse audiences and drive actionable insights.
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Infographic showing various Phd Causal Inference job openings in the United States as of September 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $122,928 per year, or $59.1 per hour.

Applied Scientist

Culver City, CA • On-site

Apple Inc.
Computer and Electronic Product Manufacturing • 10K+ employees

Other

Re-posted 14 days ago


Key responsibilities

  • Engineer end‑to‑end scalable and robust Causal Inference products to understand the health of Apple's services' marketing efforts.

  • Analyze large‑scale data sources to identify opportunities for automation, predictive methods, and modeling related to Causal Inference.

  • Collaborate with cross-functional teams to translate business requirements into technical solutions and promote the adoption of causal inference approaches.


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 684 frontline employees who took The Breakroom Quiz


Job description

Austin, Texas, United States Machine Learning and AI

Services at Apple help hundreds of millions of customers get the most out of the devices they love through amazing apps, award‑winning shows and movies, immersive music in spatial audio, world‑class workouts and meditations, super fun games and more! The Services Data Science & Analytics organization is passionate about developing discerning insights and AIML solutions to help continually improve these services and accelerate growth while maintaining a strong dedication to customer privacy.

Description

As an Applied Scientist, you will have the responsibility of pushing the boundaries of how Causal Inference and AIML can be leveraged to better serve our customers. 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 marketing effectiveness. You will also be instrumental in defining the technical vision, strategy, and execution roadmap for our AIML initiatives, ensuring that we deliver high‑quality, scalable, and impactful models that solve complex customer acquisition and engagement challenges. You will also be a key driver in fostering a vibrant culture of innovation, continuous learning, and collaborative problem‑solving.

Responsibilities
  • Engineer end‑to‑end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services’ marketing efforts.
  • Dive deep into large‑scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling.
  • Collaborate with product managers, data scientists, and other engineering teams to translate business requirements into technical specifications and deliver impactful, practical solutions, increasing internal adoption of causal inference approaches and democratizing data.
  • Stay abreast of the latest advancements in causal inference and AIML research, evaluating and integrating new frameworks where appropriate.
  • Champion best practices in software engineering, MLOps, code quality, testing, documentation, and ensure compliance with data privacy and security.
Minimum Qualifications
  • Master’s degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical field.
  • 3+ years of experience as an Applied Scientist, Machine Learning, or Data Scientist role.
  • Familiarity with a brand range of quasi‑experimental Causal Inference techniques such as diff‑in‑diff, synthetic control method, panel analysis, regression discontinuity design, interrupted time series, and propensity score matching.
  • Hands‑on experience building Marketing Mix models and validation through Matched Market testing.
  • Solid understanding of AIML technologies including Generative AI.
  • Proven track record of successfully delivering complex projects from start to finish.
  • Proficiency in programming languages such as Python, R, SQL, Java, or C++.
  • Experience with cloud platforms, Spark, Docker, and MLOps tools and best practices.
  • Excellent communication, collaboration, and presentation skills with meticulous attention to detail.
Preferred Qualifications
  • PhD in related field.
  • Hands‑on experience leveraging Generative AI to improve productivity and generate new insights.
  • Curious business attitude with an ability to condense complex concepts and models into clear and concise takeaways that drive action.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here— in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace.

Learn about reasonable accommodations for job applicants.

Apple accepts applications to this posting on an ongoing basis.

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976