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Causal Inference Jobs in Maryland (NOW HIRING)

Axle

Director of Data Solutions

Rockville, MD · On-site

Build reusable "engines" for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple ...

Axle

Director of Data Solutions

Rockville, MD · On-site

Build reusable "engines" for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple ...

Axle

Director of Data Solutions

Rockville, MD · On-site

Build reusable "engines" for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple ...

Marylandconnect

We especially welcome candidates whose research employs modern causal inference methods, machine learning, or other data-intensive empirical techniques. The successful candidatewill be expected ...

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Causal Inference information

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 Maryland? The most popular types of Causal Inference jobs in Maryland are:
What are popular job titles related to Causal Inference jobs in Maryland? For Causal Inference jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Maryland look for? The top searched job categories for Causal Inference jobs in Maryland are:
What cities in Maryland are hiring for Causal Inference jobs? Cities in Maryland with the most Causal Inference job openings:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Maryland as of July 2026, with employment types broken down into 84% Full Time, 15% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Postdoctoral Fellow - Epidemiology & Urogenital Microbiome

University of Maryland, Baltimore

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Re-posted 29 days ago

University Of Maryland, Baltimore rating

7.7

Company rating: 7.7 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

257th of 616 rated colleges and universities

Job description

Job Description
Postdoctoral Fellow - Epidemiology & Urogenital Microbiome
University of Maryland School of Medicine
Institute for Genome Sciences (IGS)
Center for Advanced Microbiome Research and Innovation (CAMRI)
The Laboratory is seeking a Postdoctoral Fellow in Epidemiology to join an NIH-funded study investigating the natural history of Mycoplasma genitalium (MG), an emerging and highly drug-resistant reproductive tract pathogen. Although MG is increasingly recognized as a cause of serious gynecologic disease, key questions about its natural history, persistence, and clinical outcomes in women remain unanswered.
The fellow will leverage extensive omics data and specimens from the NIH Longitudinal Study of Vaginal Flora, a cohort of 3,620 women followed quarterly, with more than 13,000 archived cervicovaginal specimens and detailed longitudinal clinical data, including standardized symptom assessments and pelvic examinations.
The project will examine:
How often asymptomatic, untreated MG progresses to cervicitis, pelvic inflammatory disease, and other clinical syndrome
Whether MG is independently associated with these outcomes after accounting for co-infection
How the vaginal microbiome and immune response influence progression to symptomatic disease versus spontaneous clearance
This position offers substantial opportunities to lead first-author publications, present research at national and international scientific conferences, contribute to grant development, and build an independent research program at the intersection of epidemiology, infectious diseases, microbiome science, and women's health.
Responsibilities
  • Conduct epidemiologic and statistical analyses of longitudinal clinical and high-dimensional microbiome data
  • Apply and develop methods for multi-omic data integration, including metagenomic and immune-profiling data
  • Interpret findings within epidemiologic and biological frameworks
  • Lead first-author manuscripts and co-author publications in high-impact peer-reviewed journals
  • Present research at national and international scientific meetings
  • Collaborate closely with epidemiologists, statisticians, bioinformaticians, and laboratory scientists
  • Contribute to study design, analytic plans, and grant development

Qualifications
  • PhD, ScD, or equivalent doctoral degree in epidemiology, biostatistics, bioinformatics, microbiology, or a related field
  • Strong quantitative and statistical skills, with proficiency in R or Python
  • Experience analyzing longitudinal, infectious disease, clinical, or high-dimensional data
  • Demonstrated record of peer-reviewed publications
  • Strong scientific writing skills and the ability to translate complex analyses into clear scientific narratives
  • Interest in microbiome science and women's health
  • Experience with microbiome or multi-omic data analysis, causal inference methods, or infectious disease epidemiology is preferred but not required

Research and Training Environment
The fellow will join a collaborative, intellectually rigorous, and mission-driven research environment that brings together expertise in women's health, epidemiology, biostatistics, genomics, bioinformatics, microbiology, and immunology. The position provides access to deeply characterized longitudinal cohorts, curated biospecimen repositories, and state-of-the-art genomic and computational infrastructure. Through mentorship, grant writing, and participation in future observational and interventional studies, the fellow will receive strong support in developing an independent research career.
Application
Interested applicants should submit:
  • Curriculum vitae
  • A brief statement of research interests and relevant experience
  • Contact information for three references

Applications will be reviewed on a rolling basis until the position is filled.

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