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Computational Statistics Jobs (NOW HIRING)

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Computational Biologist 1

Portland, OR · On-site

As a Computational Biologist, Level 1 , you will be focusing on advancing early cancer detection ... Experience in implementing statistical methods as appropriate for dataset treatment (e.g. quality ...

The University Of Chicago

Uses R, Python, and other applicable computational tools to perform statistical analyses, data processing, visualization, and interpretation of oncology and biological data. * Develops and applies ...

AT&T

AT&T Foundry Technical Role

Palo Alto, CA · On-site

... computational statistics and machine learning algorithms for analyzing system performance and optimization. ¥ Communicating project results clearly to both technical and non-technical audiences. ¥ ...

Fuse

Computational Physicist

San Leandro, CA · On-site

$150K - $200K/yr

About the Role As a Computational Physicist at Fuse, you'll develop the models and simulations that ... statistical mechanics, or related areas. * Strong problem-solving abilities, analytical skills, and ...

Fuse

Computational Physicist

San Leandro, CA · On-site

About the Role As a Computational Physicist at Fuse, you'll develop the models and simulations that ... statistical mechanics, or related areas. * Strong problem-solving abilities, analytical skills, and ...

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How much do computational statistics jobs pay per hour?

As of Sep 13, 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 are the key skills and qualifications needed to thrive as a computational statistician?

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 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 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 is computational statistics?

Computational statistics is a field within data analysis that uses algorithms, programming, and computer-based methods to process and interpret large datasets. It involves techniques such as simulation, optimization, and statistical modeling, often utilizing tools like R or Python. Professionals in this area develop and implement computational methods to solve complex statistical problems efficiently.
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What are popular job titles for Computational Statistics?

Popular job titles for Computational Statistics:

Computational Statistics jobs near you
Infographic showing various Computational Statistics job openings in the United States as of September 2026, with employment types broken down into 2% Internship, 79% Full Time, 18% Part Time, and 1% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

Senior Computational Biologist / Non-Tenure-Track Assistant Professor / Faculty Research Scientist

New York, NY • On-site

NYU Grossman School of Medicine
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about NYU Grossman School of Medicineexternal

Full-time

Re-posted 23 days ago

NYU Grossman School Of Medicine rating

7.9

Company rating: 7.9 out of 10

Based on 23 frontline employees who took The Breakroom Quiz

Job description

Description
The Skok Lab at NYU Grossman School of Medicine is seeking an experienced computational scientist tolead the development of computational approaches for single-molecule epigenomics and 3D genomebiology. Our research integrates Oxford Nanopore (nano-NOMe-seq) and PacBio long-read sequencing withHi-C/Hi-ChIP, single-cell multi-omics, and machine-learning approaches to investigate chromatin topology,nucleosome organization, and gene regulation.
This position provides an opportunity to lead computational strategy within a collaborative,multidisciplinary research program while developing innovative analytical methods and pursuingindependent research directions.
The successful candidate will:
  • Develop computational pipelines for long-read sequencing data, from raw signal processing to per-molecule methylation, chromatin accessibility, and chromatin-state analysis.
  • Apply statistical and machine-learning approaches to model nucleosome organization, CTCF/transcription factor binding, and RNA Polymerase II elongation.
  • Integrate nano-NOMe-seq, Hi-C/Micro-C, RNA-seq, and single-cell multiome datasets to investigatechromatin architecture and gene regulation.
  • Lead computational analyses for collaborative research projects.
  • Mentor master's students and contribute to computational training within the laboratory.
  • Develop and pursue independent computational research directions.

Appointment as a Non-Tenure-Track Assistant Professor or Senior Staff Scientist, commensurate with experience. The position is renewable, fully supported, and includes a competitive salary andcomprehensive benefits package.
Start Date: Immediate start is preferred.
Qualifications
Minimum Qualifications:
  • PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitativefield.
  • At least 5 years of postdoctoral or equivalent experience working with long-read or single-moleculesequencing data.
  • Strong programming skills (e.g., Python, R, Bash/Linux).
  • Experience with workflow automation tools such as Snakemake, Nextflow, or similar platforms.
  • Demonstrated expertise in computational epigenomics, statistical analysis, and machine learning.
  • A record of scientific innovation, leadership, and collaborative research.

Preferred Qualifications:
Experience with one or more of the following:
  • Modified-base calling tools (e.g., Remora, Megalodon, Tombo).
  • 3D genome analysis, including Hi-C, Micro-C, or related technologies.
  • Machine-learning approaches for per-molecule feature extraction, clustering, predictive modeling,deep representation learning, changepoint detection, or generative modeling.

Application Instructions
Upload CV, 1 page research statement and 3 contact references; also email materials to Jane.Skok@nuyulangone.org

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