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Causal Inference Machine Learning Postdoctoral Jobs in Pittsburgh, PA

Cmu

Associate Data Scientist

Pittsburgh, PA

$57K - $57K/yr

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

Data Scientist

Pittsburgh, PA

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

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

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Causal Inference Machine Learning Postdoctoral information

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$34.5K

$52.6K

$59.2K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 5, 2026, the average yearly pay for causal inference machine learning postdoctoral in Pittsburgh, PA is $52,641.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,900.00 and $54,900.00 per year, depending on experience, location, and employer.

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 Pittsburgh, PA? For Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA are:
What cities near Pittsburgh, PA are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Pittsburgh, PA with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you

Data Scientist

Software Engineering Institute | Carnegie Mellon University

Pittsburgh, PA • On-site

Full-time

Re-posted 29 days ago

Job description

Job Summary:
Carnegie Mellon University's Software Engineering Institute is seeking a Data Scientist to leverage advanced statistics, data analytics, machine learning, and artificial intelligence to address cybersecurity challenges. The role involves collaborating with clients to develop prototype solutions, conducting research, and presenting findings at conferences.
Responsibilities:
• Identify areas where advanced statistical techniques can help tackle problems.
• Plan and develop prototype solutions.
• Build out final products.
• Co-author research proposals.
• Execute studies and present findings to DoW sponsors and at academic conferences.
Qualifications:
Required:
• 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.
• 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)
Preferred:
• Experience in cybersecurity and privacy is a plus
• Experience in U.S. Government work and/or with FFRDCs, UARCs and National Labs is a plus
• Demonstrated ability to learn new concepts and grow into new areas of work
Company:
We conduct cutting-edge research and development that accelerates the transition of technology to the Department of War (DoW), delivering measurable impact in support of the national security mission. Founded in 1984, the company is headquartered in Pittsburgh, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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