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How much do phd causal inference jobs pay per year?

As of Jun 10, 2026, the average yearly pay for phd causal inference in Virginia is $121,874.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,100.00 and $136,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a PhD Causal Inference researcher, and why are they important?

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.

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.
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Phd Causal Inference jobs near you

Data Scientist

Carnegie Mellon University

Arlington, VA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago

Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

51st of 535 rated colleges and universities

Job description


What We Do:

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity challenges. In this role, you will work with our customers to identify areas where advanced statistical techniques can help tackle problems, plan and develop prototype solutions, and build out final products. You'll get a chance to work with elite cybersecurity professionals and university faculty to build new technologies that will influence national cybersecurity strategy for decades to come. You will co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences.
Our team works on a wide range of projects. Our current work includes research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems. Additionally, we craft metrics and experimental designs for large-scale cybersecurity research programs, develop human-in-the-loop machine learning solutions, and build classifiers to identify security vulnerabilities. If you are a data science or statistics expert with an interest in cybersecurity, we want to hear from you!
Requirements:

  • BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training or experience; or MS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with five (5) years of experience; or PhD in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with two (2) years of experience.
  • Willingness to complete modest travel to various locations to support the SEI's overall mission.
  • You will be subject to a background check and must be able obtain and maintain a U.S. Department of War security clearance.

Knowledge, Skills and Abilities:

  • Experience in predictive modeling, data science, and/or AI & machine learning
  • Deep understanding of statistical modeling techniques and advanced data analytics
  • Proficient with at least one mathematical/statistical programming package (e.g., R, python numpy/scipy/pandas/polars, MATLAB, etc.)
  • Innovative and inquisitive with ability to imagine novel analytical solutions to problems Thrives in a multi-disciplinary environment
  • Strong communication skills
  • Expertise in one or more of the following:
  • Recommendation systems
  • Time-series forecasting (Prophet, NeuralProphet, Chronos, Lag-Llama, etc.)
  • NLP / LLMs (fine-tuning, RAG, evaluation, prompt engineering)
  • Causal inference / uplift modeling / synthetic controls
  • Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow)
  • LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)
  • Experience deploying models (FastAPI, Triton, KServe, SageMaker, Vertex AI, or similar)
  • Experience working with big data (Spark, Trino, Snowflake, BigQuery, Databricks)

Desired Experience:

  • Experience in cybersecurity and privacy is a plus is a plus
  • Experience in U.S. Government work and/or with FFRDCs, UARCs an National Labs is a plus
  • Demonstrated ability to learn new concepts and grow into new areas of work

Joining the CMU team opens the door to an array of exceptional benefits.

Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits, take well-deserved breaks with ample paid time off and observed holidays, and rest easy with life and accidental death and disability insurance.

Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access, and much more!

For a comprehensive overview of the benefits available, explore our Benefits page.

At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it's about finding the perfect fit for your professional growth and personal aspirations.

Are you interested in an exciting opportunity with an exceptional organization?! Apply today!

Location

Arlington, VA, Pittsburgh, PA

Job Function

Software/Applications Development/Engineering

Position Type

Staff - Regular

Full Time/Part time

Full time

Pay Basis

Salary

More Information:

  • Please visit "Why Carnegie Mellon" to learn more about becoming part of an institution inspiring innovations that change the world.

  • Click here to view a listing of employee benefits

  • Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.

  • Statement of Assurance

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