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Computational Statistics Jobs (NOW HIRING)

Urus Group LP

Computational Biologist

Madison, WI · On-site

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Expertise in probability, statistical modeling, and algorithm design. * Computer Science:

Farm Systems

Computational Biologist

Madison, WI · On-site

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Expertise in probability, statistical modeling, and algorithm design. * Computer Science:

AgSource

Computational Biologist

Madison, WI

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Expertise in probability, statistical modeling, and algorithm design. * Computer Science:

Urus

Computational Biologist

Madison, WI

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Expertise in probability, statistical modeling, and algorithm design. * Computer Science:

AgSource

Computational Biologist

Madison, WI · On-site

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Expertise in probability, statistical modeling, and algorithm design. * Computer Science:

Lilly

Sr. Computational Statistician

Indianapolis, IN

$87K - $193K/yr

Master's degree in Statistics, Biostatistics or MSPH with concentration in Statistics or Biostatistics. Additional Skills/Preferences * Proficiency in statistical programming languages/software such ...

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How much do computational statistics jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for computational statistics 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 is the difference between Computational Statistics vs Data Scientist?

AspectComputational StatisticsData Scientist
Required CredentialsDegree in Statistics, Mathematics, or related fieldDegree in Computer Science, Statistics, or related field
Work EnvironmentResearch labs, academia, data analysis teamsTech companies, consulting firms, diverse industries
Employer & Industry UsageAcademic institutions, research organizations, analytics teamsBusiness, technology, finance, healthcare
Common Search & Comparison IntentUnderstanding specialized statistical modeling and algorithmsBroader data analysis, machine learning, and business insights

Computational Statistics focuses on developing and applying statistical algorithms and models using computational methods, often emphasizing theoretical foundations. Data Scientists utilize these techniques along with programming and data manipulation skills to extract insights from large datasets across various industries. While there is overlap, Computational Statistics is more research-oriented, whereas Data Science covers a broader range of data analysis tasks.

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

Professionals in computational statistics often encounter challenges such as managing large, complex datasets, ensuring computational efficiency, and translating statistical findings into actionable insights for non-technical stakeholders. Addressing these challenges typically involves staying updated with the latest software tools, collaborating closely with data engineers and domain experts, and continuously improving communication skills to explain technical results clearly. Proactively seeking opportunities for cross-functional teamwork and ongoing professional development can also help computational statisticians navigate these complexities and advance in their careers.

What are the key skills and qualifications needed to thrive as a Computational Statistician, and why are they important?

To thrive as a Computational Statistician, you need a solid background in statistics, mathematics, and computer science, usually supported by at least a master's degree in a related field. Expertise in programming languages such as R or Python, experience with statistical software, and familiarity with data management tools are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret data and present findings clearly. These skills are crucial for designing robust statistical models, extracting actionable insights from complex datasets, and supporting data-driven decision-making.

What is computational statistics?

Computational statistics is a field of statistics that leverages computational power to perform data analysis, statistical modeling, and inference. It involves the development and application of algorithms, simulations, and numerical methods to solve complex statistical problems that may be difficult or impossible to address analytically. This field is essential for handling large datasets, implementing advanced statistical techniques, and supporting data-driven decision making in various scientific and industrial domains.
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Computational Statistics jobs near you
Infographic showing various Computational Statistics job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $114,249 per year, or $54.9 per hour.
Junior Computational Biologist (Remote)

Junior Computational Biologist (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$30 - $34/hr

Full-time

Posted 27 days ago

Job description

Pay Rate Low: 30 | Pay Rate High: 34
A leading biotechnology research organization is seeking a Junior Computational Biologist to support efforts in refining how cellular states are quantified and validated!
Title: Jr. Computational Biologist (Remote Contract)
Location: Remote (Must be available during PST business hours)
Compensation: $30-34/hour + benefits
Contract Duration: 6-12+ months
Job Duties:
This project will focus on benchmarking functional scoring methodologies and improving interpretability of high-dimensional transcriptomic datasets.
The selected candidate will contribute to distinguishing true biological signal from technical variation in large-scale single-cell atlases, directly enhancing the reliability of automated cell-state classification frameworks.
Start Date: July 1, 2026
  • Duration: Through December 18, 2026
  • Commitment: Full-time (100%)
  • Ideal Candidate: Upcoming June 2026 PhD graduate or recent PhD graduate
  • Location: Onsite in South San Francisco, CA preferred; remote within the U.S. considered (must work PST hours)
  • Visa Sponsorship: Not availabl

Key Responsibilities
  • Systematically evaluate and benchmark computational approaches for quantifying phenotype activation across single-cell transcriptomic datasets.
  • Establish rigorous statistical baselines and negative-control frameworks to improve the robustness of automated cell-state classification methods.
  • Develop or refine computational methods to address limitations in current approaches.
  • Design strategies to distinguish genuine biological signatures from stochastic or technical noise.
  • Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.

Required Qualifications
  • Extensive hands-on experience in single-cell data analysis using Scanpy, AnnData, and Pandas.
  • Strong proficiency implementing statistical and machine learning models using scikit-learn and SciPy.
  • Demonstrated commitment to reproducible research practices and well-organized code.
  • Ability to clearly communicate complex computational concepts to interdisciplinary scientific teams.
  • Master's degree with ongoing PhD pursuit, or recent PhD graduate, in Computational Biology, Computer Science, Machine Learning, or related quantitative discipline.
  • Interest in drug discovery and comfort working in dynamic, research-driven environments.

Preferred Qualifications
  • Background knowledge in cell biology and/or immunology.
  • Experience with hypothesis testing, noise modeling, and benchmarking computational tools.
  • Familiarity with Explainable AI (XAI) approaches or large-scale biological datasets.
  • Demonstrated ability to build or extend novel bioinformatics pipelines.
    INDBH
    #LI-MG1

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