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Computational Genomics Research Scientist

Boston, MA · Hybrid

$112K - $168K/yr

We are seeking a Computational Genomics Research Scientist to support the development of transformative cell and gene therapies across Vertex's research portfolio. This Boston-based position is part ...

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How much do computational scientist jobs pay per year?

As of Sep 2, 2026, the average yearly pay for computational scientist in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What is a computational scientist?

A Computational Scientist uses advanced computing techniques, algorithms, and simulations to solve complex scientific and engineering problems. They work across various fields, such as physics, biology, chemistry, and engineering, developing models and running simulations to analyze large datasets. Their role often involves programming, data analysis, and collaboration with domain experts to gain insights and make predictions.

What does a computational scientist do?

A typical day for a Computational Scientist may involve developing and running computer simulations, analyzing large datasets, or creating algorithms to address complex scientific problems. You might spend time coding, troubleshooting computational models, collaborating with researchers or engineers, and documenting your findings. The work often involves both independent problem-solving and teamwork within multidisciplinary groups. Additionally, presenting results, preparing research papers, or contributing to grant proposals could be regular tasks, depending on your specific employer and industry. This dynamic role offers a blend of technical challenges and opportunities for innovation.

What skills and qualifications are needed to thrive as a computational scientist?

To thrive as a Computational Scientist, you need strong analytical skills, advanced knowledge of mathematics and computer science, and typically a graduate degree in a quantitative field such as physics, engineering, or computer science. Familiarity with programming languages like Python, R, or C++, experience with high-performance computing, and tools such as MATLAB or scientific visualization software are vital. Excellent problem-solving abilities, collaboration, and effective communication skills make someone stand out in this position. These skills are crucial for developing models, analyzing complex data, and communicating scientific insights to interdisciplinary teams.

What are the career paths in computational science?

Computational scientists can pursue career paths in academia, industry, or government research, often specializing in areas like data analysis, modeling, or simulation. They may advance to senior scientist, research director, or interdisciplinary roles, and typically develop skills in programming, statistical analysis, and domain-specific knowledge. Certifications in programming languages or data management can enhance career progression.
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Computational Scientist jobs near you
Infographic showing various Computational Scientist job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 91% Full Time, 4% Part Time, and 3% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

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

NYU Grossman School of Medicine

New York, NY • On-site

Full-time

Re-posted 12 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

213th of 628 rated colleges and universities

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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