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Causal Inference Jobs in Pittsburgh, PA (NOW HIRING)

Associate Data Scientist

Pittsburgh, PA

$57K - $57K/yr

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

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Showing results 1-20

Causal Inference information

See Pittsburgh, PA salary details

$53.4K

$96.3K

$131.5K

How much do causal inference jobs pay per year?

As of Jul 26, 2026, the average yearly pay for causal inference in Pittsburgh, PA is $96,335.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $105,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are the most commonly searched types of Causal Inference jobs in Pittsburgh, PA? The most popular types of Causal Inference jobs in Pittsburgh, PA are:
What are popular job titles related to Causal Inference jobs in Pittsburgh, PA? For Causal Inference jobs in Pittsburgh, PA, the most frequently searched job titles are:
Infographic showing various Causal Inference job openings in Pittsburgh, PA as of July 2026, with employment types broken down into 83% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $96,335 per year, or $46.3 per hour.
Assistant Professor in Marketing

Assistant Professor in Marketing

University of Pittsburgh

Pittsburgh, PA • On-site

Full-time

Posted 25 days ago


Job description

The School of Business at the University of Pittsburgh seeks an Assistant Professor in Marketing in Pittsburgh, PA, to teach graduate and undergraduate courses and produce high-quality research that results in publications in top journals. Duties include: (i) teaching undergraduate business students; (ii) conducting research on empirical quantitative marketing topics and leading research projects that result in publishing in top journals; (iii) advising PhD students specializing in quantitative marketing; and (iv) participating in departmental and academic services, including faculty hiring, PhD admission, curriculum design, conference presentations, and peer review.
Must have a PhD degree (or foreign equivalent degree) in Marketing, Management, or a related field.
Must also have any experience with: (i) teaching marketing classes to MBA/EMBA students; and (ii) conducting independent research and leading research projects in empirical quantitative marketing on topics related to competition policy, privacy, platforms, digitization, and retailing.
Must also have: (i) evidence of publication of at least two (2) peer-reviewed journal articles for top marketing and management journals; (ii) at least two (2) single authored academic paper and two (2) paper coauthored with other scholars, (iii) presentations of scholarly work at major academic marketing and economics conferences; and (iv) PhD level coursework in economic theory, econometrics, statistics, causal inference, and major areas of marketing research.
Experience can be concurrent.
Apply at https://www.join.pitt.edu, #26003872.
Please answer the screening questions by accessing the link here: Screening Questions_Assistant Professor, Marketing (2026).docx