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)
Data Scientist - Marketing
Lititz, PA · On-site +1
Hands-on experience with experimentation and causal inference frameworks, including incrementality testing, geo experiments, holdout designs, or quasi-experimental methods. * Proven ability to ...
Data Scientist - Marketing
Lititz, PA · On-site +1
Hands-on experience with experimentation and causal inference frameworks, including incrementality testing, geo experiments, holdout designs, or quasi-experimental methods. * Proven ability to ...
... and causal inference. * Experience working with high-dimensional, large-scale molecular and cellular datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic/lipidomic, imaging ...
... and causal inference. * Experience working with high-dimensional, large-scale molecular and cellular datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic/lipidomic, imaging ...
... causal inference, synthetic data, reproducibility, intersection of AI and data privacy, and work with survey, census, health/genomic and other complex data. The scholar will have an opportunity to ...
... causal inference, synthetic data, reproducibility, intersection of AI and data privacy, and work with survey, census, health/genomic and other complex data. The scholar will have an opportunity to ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Familiarity with analytical methods (e.g., causal inference, propensity matching) and their application to real-world data * Familiarity with data science workflows If you will be working at home ...
Familiarity with analytical methods (e.g., causal inference, propensity matching) and their application to real-world data * Familiarity with data science workflows If you will be working at home ...
Postdoctoral Scholar
University Park, PA · On-site
The postdoctoral scholar is expected to work on a set of statistical-theoretical problems concerned with networks, space, time, or causal inference based on discrete and dependent data, either ...
Postdoctoral Scholar
University Park, PA · On-site
The postdoctoral scholar is expected to work on a set of statistical-theoretical problems concerned with networks, space, time, or causal inference based on discrete and dependent data, either ...
The postdoctoral scholar is expected to work on a set of statistical-theoretical problems concerned with networks, space, time, or causal inference based on discrete and dependent data, either ...
The postdoctoral scholar is expected to work on a set of statistical-theoretical problems concerned with networks, space, time, or causal inference based on discrete and dependent data, either ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
Knowledge of experimental design and causal inference techniques. * Experience in research & development, government contracting, and/or highly regulated industry domains. Why CTC? * Our teams at CTC ...
RWE Scientist / Immunology Studies / Remote Work
Spring House, PA · On-site
$56K - $56K/yr
Familiarity with machine learning, causal inference, and advanced statistical modeling. Prior publications in peer-reviewed journals and presentations at global conferences.
Quick apply
RWE Scientist / Immunology Studies / Remote Work
Spring House, PA · On-site
$56K - $56K/yr
Familiarity with machine learning, causal inference, and advanced statistical modeling. Prior publications in peer-reviewed journals and presentations at global conferences.
What We Do The SEI's Applied Measurement & Experimentation (AME) team develops analytic workflows, measurement tools, causal inference capabilities, and robust data pipelines that support engineering ...
What We Do The SEI's Applied Measurement & Experimentation (AME) team develops analytic workflows, measurement tools, causal inference capabilities, and robust data pipelines that support engineering ...
Senior Specialist, Data Science
$129K - $203K/yr
Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference. * Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...
Senior Specialist, Data Science
$129K - $203K/yr
Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference. * Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...
Strong knowledge of statistical methods, hypothesis testing, regression analysis, causal inference, forecasting, and experimental design. * Demonstrated ability to independently scope, prioritize ...
Strong knowledge of statistical methods, hypothesis testing, regression analysis, causal inference, forecasting, and experimental design. * Demonstrated ability to independently scope, prioritize ...
What We Do The SEI's Applied Measurement & Experimentation (AME) team develops analytic workflows, measurement tools, causal inference capabilities, and robust data pipelines that support engineering ...
What We Do The SEI's Applied Measurement & Experimentation (AME) team develops analytic workflows, measurement tools, causal inference capabilities, and robust data pipelines that support engineering ...
Strong knowledge of statistical methods, hypothesis testing, regression analysis, causal inference, forecasting, and experimental design. Demonstrated ability to independently scope, prioritize, and ...
Strong knowledge of statistical methods, hypothesis testing, regression analysis, causal inference, forecasting, and experimental design. Demonstrated ability to independently scope, prioritize, and ...
A/B testing and causal inference a plus. Experience supporting value based care, provider performance analytics, and provider engagement. Physical Demands Lift and carry 25 lbs. frequent sitting ...
A/B testing and causal inference a plus. Experience supporting value based care, provider performance analytics, and provider engagement. Physical Demands Lift and carry 25 lbs. frequent sitting ...
Experience with causal inference methods, longitudinal data analysis, and advanced epidemiologic methods. * Experience developing and analyzing dietary patterns derived from Food Frequency ...
Experience with causal inference methods, longitudinal data analysis, and advanced epidemiologic methods. * Experience developing and analyzing dietary patterns derived from Food Frequency ...
Senior Specialist, Data Science
West Point, PA · On-site
$129K - $203K/yr
Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference. * Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...
Senior Specialist, Data Science
West Point, PA · On-site
$129K - $203K/yr
Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference. * Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...
Causal Inference information
See Pennsylvania salary details
$55.1K - $62.5K
0% of jobs
$62.5K - $69.8K
1% of jobs
$69.8K - $77.1K
3% of jobs
$77.1K - $84.5K
17% of jobs
$86K is the 25th percentile. Wages below this are outliers.
$84.5K - $91.8K
18% of jobs
The median wage is $96.4K / yr.
$91.8K - $99.1K
17% of jobs
$99.1K - $106.5K
18% of jobs
$106.7K is the 75th percentile. Wages above this are outliers.
$106.5K - $113.8K
17% of jobs
$113.8K - $121.2K
6% of jobs
$121.2K - $128.5K
1% of jobs
$128.5K - $135.8K
1% of jobs
$55.1K
$99.5K
$135.8K
How much do causal inference jobs pay per year?
As of Aug 12, 2026, the average yearly pay for causal inference in Pennsylvania is $99,469.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,200.00 and $108,800.00 per year, depending on experience, location, and employer.
Is causal inference still relevant?
Causal inference is a vital skill for data analysts and researchers, as it helps determine cause-and-effect relationships in data. It remains highly relevant across industries such as healthcare, economics, and technology, especially with the increasing availability of large datasets and advanced statistical tools like R and Python. Professionals in this field are in demand for designing experiments, analyzing observational data, and informing decision-making processes.
What skills and qualifications are needed for a causal inference position?
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 jobs use causal inference?
Causal inference is used in various roles such as data scientist, epidemiologist, econometrician, and policy analyst. These jobs involve analyzing data to determine cause-and-effect relationships, often using statistical tools and programming languages like R or Python. Professionals in these fields work in industries like healthcare, finance, government, and technology to inform decision-making and policy development.
What are 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?
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 Pennsylvania? The most popular types of Causal Inference jobs in Pennsylvania are:
What are popular job titles related to Causal Inference jobs in Pennsylvania? For Causal Inference jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Pennsylvania look for? The top searched job categories for Causal Inference jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Causal Inference jobs? Cities in Pennsylvania with the most Causal Inference job openings:
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 ten (10) 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 eight (8) years of experience; or PhD in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with five (5) 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, PAJob Function
Software/Applications Development/EngineeringPosition Type
Staff - RegularFull time/Part time
Full timePay 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
About CMU
Sourced by ZipRecruiter
Industry
Offices of mental health practitioners
Company size
201 - 500 Employees
Headquarters location
Harrisburg, PA, US