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How much do computational research jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for computational research in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals in computational research, and how can they be addressed?

Professionals in computational research often encounter challenges such as managing large datasets, optimizing algorithms for efficiency, and ensuring reproducibility of results. Collaborating effectively with interdisciplinary teams—such as domain scientists, software engineers, and data analysts—can also be complex due to differing technical backgrounds. To address these challenges, it's essential to stay updated on the latest tools and best practices, document code thoroughly, and communicate regularly with collaborators to align goals and methodologies. Many organizations also offer mentorship programs and workshops to support ongoing professional development in these areas.

What is computational research?

Computational research involves using computer-based models, simulations, and data analysis to solve complex scientific and engineering problems. Researchers in this field use algorithms, software, and high-performance computing to analyze large datasets, perform simulations, and make predictions. This approach is widely used across disciplines such as physics, biology, chemistry, and engineering to complement experimental and theoretical work.

What is the difference between Computational Research vs Data Scientist?

AspectComputational ResearchData Scientist
Required CredentialsAdvanced degrees in computer science, mathematics, or related fieldsDegree in statistics, computer science, or related fields; certifications like SAS or Python
Work EnvironmentResearch labs, academic institutions, R&D departmentsCorporate settings, tech companies, finance, healthcare
Industry UsagePrimarily in academia, government, and research institutionsWidely in business, tech, and consulting sectors

Computational Research focuses on developing new algorithms and models for scientific discovery, often in academic or research settings. Data Scientists analyze and interpret large datasets to inform business decisions. While both roles require strong technical skills and programming knowledge, Computational Research emphasizes theoretical development, whereas Data Scientists focus on practical data analysis and insights.

What are the key skills and qualifications needed to thrive in Computational Research, and why are they important?

To excel in Computational Research, a strong background in mathematics, computer science, and domain-specific knowledge is essential, often supported by advanced degrees such as a master's or PhD. Proficiency with programming languages (like Python, R, or C++), data analysis tools, and high-performance computing systems is typically required. Critical thinking, problem-solving, and effective collaboration are crucial soft skills for success in interdisciplinary research environments. These skills and qualities are important because they enable researchers to design innovative solutions, analyze complex data, and drive scientific discovery.
More about Computational Research jobs
What are the most commonly searched types of Computational Research jobs? The most popular types of Computational Research jobs are:
What job categories do people searching Computational Research jobs look for? The top searched job categories for Computational Research jobs are:
Computational Research jobs near you
Infographic showing various Computational Research job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 2% Internship, 70% Full Time, 25% Part Time, 1% Temporary, and 1% Contract. Highlights an 70% Physical, 1% Hybrid, and 29% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

Computational Research Associate

Neptune Bio

New York, NY

Other

Posted 5 days ago

Job description

Position Summary

We are seeking a Computational Research Associate to support our data analysis and infrastructure efforts across functional genomics and single-cell perturbation experiments. The ideal candidate has hands-on experience working with large biological datasets, enjoys building efficient and reproducible analysis workflows, and thrives in a collaborative, fast-paced startup environment.

You will play a central role in processing, analyzing, and organizing single-cell and perturb-seq data, maintaining and improving computational pipelines, and supporting the broader team with high-quality data outputs and infrastructure.

Key Responsibilities

  • Process and analyze large-scale single-cell and perturb-seq datasets using established computational pipelines.
  • Develop, document, and maintain reproducible analysis workflows and data processing infrastructure.
  • Support data management and organization across multiple internal and external datasets.
  • Collaborate closely with experimental and computational scientists to translate raw data into interpretable biological results.
  • Implement and optimize pipelines in cloud environments (e.g., AWS, GCP) for scalable data processing.
  • Maintain codebases, perform quality control on data outputs, and ensure reproducibility and traceability of analyses.
  • Generate clear reports, visualizations, and summaries to communicate results across teams.

Qualification and Education Requirements

You must have:

  • B.S. or M.S. in Bioinformatics, Computational Biology, Computer Science, or a related quantitative field.
  • 2+ years of experience working with biological or single-cell datasets.
  • Proficiency in Python and/or R for data analysis and visualization.
  • Familiarity with standard genomics tools and file formats (FASTQ, BAM, HDF5, AnnData, etc.).
  • Experience using and maintaining analysis pipelines in a Unix/Linux environment.
  • Experience working with cloud compute platforms (AWS, GCP, or similar).
  • Strong organizational skills, attention to detail, and commitment to clean, reproducible code.

Additional preferred experience includes:

  • Experience analyzing single-cell RNA-seq or perturb-seq datasets.
  • Familiarity with workflow management systems (Nextflow, Snakemake, or similar).
  • Experience with containerization tools such as Docker.
  • Exposure to data engineering concepts (e.g., databases, versioned data storage, data pipelines).
  • Understanding of basic statistical methods for genomics data analysis.
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