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Causal Inference Jobs in Baltimore, MD (NOW HIRING)

Umd

Senior Faculty Specialist

College Park, MD

Familiarity with experimental design and causal inference methodologies *Familiarity with or interest in applying generative AI techniques *Excellent communication skills, passion for educational ...

US Department of the Treasury

Data Scientist

Lanham, MD · On-site

$106K/yr

Experience applying statistical methods, hypothesis testing, experimental design, causal inference, and model evaluation techniques to support data-driven decision making. * Experience working with ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Laurel, MD · Remote

$26 - $40/hr

... and inference questions. Ability to explain argument structure, conditional logic, causal reasoning patterns, and formal logic principles while preparing students for competitive law school ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Bowie, MD · Remote

$26 - $40/hr

... and inference questions. Ability to explain argument structure, conditional logic, causal reasoning patterns, and formal logic principles while preparing students for competitive law school ...

All JobsCausal Inference JobsCausal Inference Jobs in Baltimore, MD

Causal Inference information

See Baltimore, MD salary details

$54.6K

$98.6K

$134.6K

How much do causal inference jobs pay per year?

As of Jul 24, 2026, the average yearly pay for causal inference in Baltimore, MD is $98,600.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,500.00 and $107,800.00 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 Baltimore, MD? The most popular types of Causal Inference jobs in Baltimore, MD are:
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What job categories do people searching Causal Inference jobs in Baltimore, MD look for? The top searched job categories for Causal Inference jobs in Baltimore, MD are:
What cities near Baltimore, MD are hiring for Causal Inference jobs? Cities near Baltimore, MD with the most Causal Inference job openings:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Baltimore, MD as of July 2026, with employment types broken down into 91% Full Time, 8% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $98,600 per year, or $47.4 per hour.
Postdoctoral Fellow - CausalML Lab

Postdoctoral Fellow - CausalML Lab

Johns Hopkins University

Baltimore, MD • On-site

$48K - $66K/yr

Full-time

Posted 20 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 205 frontline employees who took The Breakroom Quiz

230th of 890 rated healthcare providers

Job description

Description
Dr. Murat Kocaoglu is seeking highly motivated postdoctoral scholars to join CausalML lab at Johns Hopkins University in 2026. This position offers the opportunity to work directly with CausalML Lab (led by Dr. Kocaoglu), alongside its respective lab members.
The research focuses on developing fundamental algorithms for causal decision making using real-world, non-IID time series datasets. This work is primarily conducted at the Homewood Campus in Baltimore. The ideal candidate has experience with developing algorithms for non-IID or time-series data.
Applicants with strong credentials and motivation should still apply even if they lack these specific qualifications. This is a full-time position with an initial appointment duration of 12 months, and the possibility of further extension based on performance and funding.
Qualifications
Qualifications:
  • PhD in Computer Science, Statistics, Electrical Engineering or a related field.
  • Strong publication record in peer-reviewed venues (e.g., NeurIPS, ICML, UAI, ICLR, AAAI, AISTATS) relevant to ML or AI.
  • Strong understanding of fundamental causal inference and causal discovery algorithms.
  • Experience with the design, development and implementation of causal ML algorithms.
  • Ability to lead research projects independently from concept to implementation.
  • Capability to formulate research methodologies and manage project timeline.
  • High proficiency in Python and deep learning frameworks (e.g., PyTorch).
  • Excellent teamwork skills for working in an interdisciplinary environment involving engineers, computer scientists, and clinicians.
  • Ability to work effectively in a highly collaborative, multi-lab environment.

Application Instructions
Applicants must submit the following documents via this Interfolio posting:
  • CV
  • Contact information for two (2) references.

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