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Causal Inference Machine Learning Postdoctoral Jobs in Pennsylvania

Cmu

Data Scientist

Pittsburgh, PA · On-site

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

Cmu

Senior Data Scientist

Pittsburgh, PA · On-site

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

The Pennsylvania State University

POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

They are seeking a Director of Machine Learning to define the ML strategy, lead the computer vision ... embedded inference) • Familiarity with warehouse, logistics, or supply chain domain • ...

Nuuly

Nuuly Lead Data Scientist

Philadelphia, PA · On-site

Manage machine learning and statistical models to address various business needs * Demonstrate ... Conduct causal inference studies and exploratory analyses to measure the impact of strategic ...

Nuuly

Nuuly Lead Data Scientist

Philadelphia, PA

Manage machine learning and statistical models to address various business needs * Demonstrate ... Conduct causal inference studies and exploratory analyses to measure the impact of strategic ...

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Causal Inference Machine Learning Postdoctoral information

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

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

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Pennsylvania? For Causal Inference Machine Learning Postdoctoral jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Pennsylvania look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Pennsylvania with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Pennsylvania as of July 2026, with employment types broken down into 3% Locum Tenens, 77% Full Time, 16% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Data Scientist

Cmu

Pittsburgh, PA • On-site

Full-time

Re-posted 28 days ago

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

Location

Arlington, VA, Pittsburgh, PA

Job Function

Software/Applications Development/Engineering

Position Type

Staff - Regular

Full time/Part time

Full time

Pay Basis

SalaryMore 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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About CMU

Sourced by ZipRecruiter

Industry

Offices of mental health practitioners

Company size

201 - 500 Employees

Headquarters location

Harrisburg, PA, US

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