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Causal Inference Jobs in Durham, NC (NOW HIRING)

Duke University

Postdoctoral Associate

Durham, NC · On-site

Familiarity with causal inference methods or machine learning approaches * Demonstrated experience in scientific writing and publication Ideal for candidates who: * Have recently completed (or are ...

Duke University

Postdoctoral Associate

Durham, NC

Familiarity with causal inference methods or machine learning approaches * Demonstrated experience in scientific writing and publication Ideal for candidates who: * Have recently completed (or are ...

Epic Games

Senior Data Scientist

Cary, NC · On-site

Experience with causal inference methods * Passion for the gaming industry EPIC JOB + EPIC BENEFITS = EPIC LIFE Our intent is to cover all things that are medically necessary and improve the quality ...

Western Governors University

Decision Scientist

Raleigh, NC · On-site

$118K - $178K/yr

Apply advanced analytics, experimentation, and causal inference techniques to identify opportunities that improve student experiences and outcomes. Partner Across the Organization * Collaborate with ...

Western Governors University

Decision Scientist

Raleigh, NC · On-site +1

$118K - $178K/yr

Apply advanced analytics, experimentation, and causal inference techniques to identify opportunities that improve student experiences and outcomes. Partner Across the Organization * Collaborate with ...

Western Governors University

Decision Scientist

Raleigh, NC · On-site

$118K - $178K/yr

Apply advanced analytics, experimentation, and causal inference techniques to identify opportunities that improve student experiences and outcomes. Partner Across the Organization * Collaborate with ...

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All JobsCausal Inference JobsCausal Inference Jobs in Durham, NC

Causal Inference information

See Durham, NC salary details

$53.1K

$95.9K

$130.9K

How much do causal inference jobs pay per year?

As of Aug 9, 2026, the average yearly pay for causal inference in Durham, NC is $95,887.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,100.00 and $104,800.00 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 Durham, NC? The most popular types of Causal Inference jobs in Durham, NC are:
What are popular job titles related to Causal Inference jobs in Durham, NC? For Causal Inference jobs in Durham, NC, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Durham, NC look for? The top searched job categories for Causal Inference jobs in Durham, NC are:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Durham, NC as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 70% Physical, 2% Hybrid, and 28% Remote job distribution, with an average salary of $95,887 per year, or $46.1 per hour.

Principal Statistical Methodologist, Raleigh, NC

UCB

Raleigh, NC • On-site

Full-time

Posted 17 days ago

Job description

Make your mark for patients

We are looking for a Principal Statistical Methodologist who is curious, collaborative, and strategic to join our Statistical Innovation team within Biometric and Data Sciences (BDS), based in any of our Braine l'Alleud (Belgium), Monheim (Germany), Slough (UK) or Raleigh (US) offices.

About the role

You will help shape how evidence is generated, modeled, and communicated across the drug development lifecycle, bringing modern computational and machine-learning methods to bear on real R&D decisions. You will develop and apply advanced data-driven and model-based approaches-drawing on statistics, machine learning, and AI-translate them into robust, reusable tools, and partner across functions to put them to work where they impact drug development decision making. You will also contribute to the group's scientific profile through publications and external collaboration.

Who you will work with

You will be working in a team that provides statistical consultancy across therapy areas and development stages, partnering closely with colleagues in clinical development, regulatory strategy, data science, and medical affairs. The team values curiosity and problem solving, practical innovation, clear communication, and collaboration, bringing novel quantitative and computational approaches into real study decisions and sharing learnings with the wider scientific community.

What you will do

  • Develop and apply advanced computational and statistical methods, including machine learning, AI, and scenario evaluation using modern simulation approaches, to inform design, analysis, and decision-making across development.
  • Build robust, well-engineered, reusable tools and workflows that bring these methods into routine use, with attention to reproducibility and software quality.
  • Bring a quantitative lens with appropriate rigor to emerging problems such as synthetic and external control data, causal inference, and digital-twin or simulation-based approaches.
  • Partner with statisticians and cross-functional colleagues to identify where computational and data-driven methods add the most leverage, and translate complex approaches into clear insight for technical and non-technical audiences.
  • Contribute to internal capability building by sharing tools, code, and methods across the team and wider organization.
  • Contribute to the group's external profile through scientific publications, conference presentations, and participation in cross-industry initiatives and working groups.

Interested? Here is what we are looking for

  • Doctoral degree in statistics, biostatistics, mathematics, computer science with a strong quantitative/statistical component, or a closely related discipline with a solid grounding in statistical inference and uncertainty.
  • 3+ years of experience within the pharmaceutical industry. Experience in advanced computational methodology for clinical development (early to late stage) is an advantage. Direct entry may be considered.
  • Strong, multi-language scientific programming skills (R and Python preferred; software-engineering practices such as version control, testing, and reproducible workflows a clear advantage).
  • Demonstrated expertise in machine learning and/or AI methods, with hands-on experience applying them to real problems; experience with large language models, causal inference, synthetic data, or digital-twin/simulation approaches is a strong advantage.
  • Sound knowledge of ICH guidelines and understanding of regulatory requirements from major health authorities.
  • Ability to work effectively with autonomy, manage multiple priorities, and deliver timely, high-quality outputs.
  • Clear written and spoken communication in English, including the ability to explain technical concepts to non-technical audiences.

Internal applicants should be in their current job for at least 12 months, must meet performance standards and are not on formal corrective/disciplinary process (PIP), warning, final warning, or compliance warning letters within the last 12 months. Please inform your Manager or your Talent Partner before applying to any internal job opportunities.

Unlessexplicitlystated in the description, this role is hybrid with 40% of your time spent in the office,regardlessof your current contractual agreement. If your current working arrangements differ, please contact your Talent Partner to discuss before submitting your application.

UCB is an equal opportunity employer. All employment decisions will be made without regard to any characteristic protected by applicable federal, state, or local law. UCB invites you to voluntarily self-identify during the application process. Provision of self-identification information is entirely voluntary and a decision to provide or not provide such information will not have any effect on your application for employment, your employment with UCB, or otherwise subject you to any adverse treatment. Any information you provide will be considered confidential and will be kept separate from your application and/or personnel file and will only be used in accordance with applicable laws, orders, and regulations.


Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities contact us on US-Reasonable_Accommodation@ucb.com for application to US based roles. Please note should your enquiry not relate to adjustments; we will not be able to support you through this channel.

Requisition ID:93650

Recruiter:Kevin Ross

Hiring Manager:Baldur Magnusson

Talent Partner:Natacha Tassier

Job Level:MM II

Please consult HRAnswers for more information on job levels.

Employment Type: OTHER

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