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Freelance Bioinformatics Machine Learning Jobs in Secaucus, NJ

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

Our primary focus is the analysis of high-dimensional biomedical data integrating statistical modelling, bioinformatics and machine learning to develop predictive models of diagnosis and prognosis ...

Formation Bio

Principal Data Scientist

New York, NY

$204K - $267K/yr

... bioinformatics analysis Preferred Qualifications * Proven expertise in cloud computing environments, including proficiency with tabular and/or graph databases * Strong background in machine learning ...

Formation Bio

Senior Data Scientist

New York, NY · Hybrid

$177K - $232K/yr

... bioinformatics analysis Preferred Qualifications * Proven expertise in cloud computing environments, including proficiency with tabular and/or graph databases * Strong background in machine learning ...

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

A major component of this work will rely on applying machine learning methods to large-scale ... bioinformatics, computational biology, or other fields with strong quantitative skills will be ...

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Freelance Bioinformatics Machine Learning information

See Secaucus, NJ salary details

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How much do freelance bioinformatics machine learning jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for freelance bioinformatics machine learning in Secaucus, NJ is $31.77, according to ZipRecruiter salary data. Most workers in this role earn between $12.21 and $51.35 per hour, depending on experience, location, and employer.

What does a freelance bioinformatics machine learning specialist do?

A Freelance Bioinformatics Machine Learning specialist applies machine learning techniques to analyze biological data, such as genomics, proteomics, and medical records, on a project-by-project basis. They typically work independently with research labs, biotech companies, or healthcare organizations to develop algorithms, build predictive models, and interpret complex biological datasets. Their work helps drive insights in areas like drug discovery, personalized medicine, and disease prediction, often leveraging tools like Python, R, and specialized bioinformatics software. As freelancers, they have the flexibility to choose projects, set their schedules, and work remotely.

What are the key skills and qualifications needed to thrive as a freelance bioinformatics machine learning specialist?

To thrive as a Freelance Bioinformatics Machine Learning Specialist, you need a strong background in biology, statistics, and programming (such as Python or R), typically supported by a relevant degree in bioinformatics, computer science, or a related field. Familiarity with bioinformatics tools (e.g., BLAST, Bioconductor), machine learning libraries (scikit-learn, TensorFlow), and experience with cloud computing platforms are highly valuable. Strong problem-solving, communication, and project management skills help distinguish top freelancers in this field. These capabilities are crucial for independently delivering accurate, actionable biological insights to clients and efficiently managing multiple projects.

What are some common challenges freelance bioinformatics machine learning professionals face when working with multiple clients?

Freelance bioinformatics machine learning professionals often encounter challenges such as managing diverse data formats, aligning project expectations, and ensuring data privacy across multiple clients. Each client may have unique datasets, varying levels of documentation, and different computational infrastructure, requiring adaptability and strong communication skills. Balancing multiple deadlines and maintaining clear, consistent reporting are also important to foster trust and long-term collaborations.

What is the difference between Freelance Bioinformatics Machine Learning vs Freelance Data Scientist?

AspectFreelance Bioinformatics Machine LearningFreelance Data Scientist
CredentialsBackground in bioinformatics, biology, or related fields; knowledge of machine learningBackground in statistics, computer science, or related fields; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic projects, freelance consultingVarious industries including finance, tech, healthcare, consulting
Industry UsagePrimarily biotech, healthcare, genomics, pharmaceutical sectorsBroad industry application including finance, marketing, tech, healthcare

Freelance Bioinformatics Machine Learning specialists focus on applying machine learning techniques to biological data, often working within biotech and healthcare sectors. In contrast, Freelance Data Scientists have a broader scope, working across multiple industries with diverse datasets. Both roles require strong analytical skills and programming expertise, but their industry focus and domain knowledge differ significantly.

What job categories do people searching Freelance Bioinformatics Machine Learning jobs in Secaucus, NJ look for?

The top searched job categories for Freelance Bioinformatics Machine Learning jobs in Secaucus, NJ are:

What cities near Secaucus, NJ are hiring for Freelance Bioinformatics Machine Learning jobs?

Cities near Secaucus, NJ with the most Freelance Bioinformatics Machine Learning job openings:

Freelance Bioinformatics Machine Learning jobs near you

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

NYU Langone Health

Manhattan, NY

Full-time

Posted 17 days ago

NYU Langone Health rating

8.5

Company rating: 8.5 out of 10

Based on 247 frontline employees who took The Breakroom Quiz

13th of 887 rated healthcare providers

Job 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: Flexible. Immediate start available, with a preferred start date anytime between now and October 2026.

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.

NYU Langone Health is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration.

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