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Remote Bioinformatics Machine Learning Jobs in Connecticut

Relativity

Senior Applied Scientist

Bridgeport, CT · On-site +1

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... Develop machine learning and generative AI models that ship as customer-facing product features

The Hartford

Data Scientist

Hartford, CT · On-site +1

$90.16K - $135.24K/yr

... remote work arrangement, with the expectation of coming into an office as business needs arise. Responsibilities: * Create statistical models, algorithms, and machine learning techniques to enhance ...

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Remote Bioinformatics Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote Bioinformatics Machine Learning Specialist, and why are they important?

To excel as a Remote Bioinformatics Machine Learning Specialist, a strong background in computational biology, statistics, and machine learning—often supported by an advanced degree in bioinformatics, computer science, or a related field—is essential. Proficiency with programming languages like Python or R, experience using machine learning frameworks (such as TensorFlow or scikit-learn), and familiarity with bioinformatics tools and databases are typically required. Excellent problem-solving, self-motivation, and clear communication skills help professionals collaborate effectively and independently in remote environments. These abilities are vital for developing accurate models, interpreting complex biological data, and contributing meaningful insights to scientific research.

How do remote bioinformatics machine learning professionals typically collaborate with cross-functional teams?

Remote bioinformatics machine learning professionals often work closely with biologists, data scientists, and software engineers. Collaboration is typically facilitated through virtual meetings, shared code repositories, and project management tools. Regular communication is essential to align on data requirements, model development, and interpretation of results. While remote work offers flexibility, it requires strong organizational skills and proactive engagement to ensure seamless teamwork and project success.

What is a Remote Bioinformatics Machine Learning specialist?

A Remote Bioinformatics Machine Learning specialist is a professional who applies machine learning techniques to biological data, such as genomics or proteomics, while working from a remote location. They analyze complex biological datasets to uncover patterns, make predictions, and contribute to advancements in areas like drug discovery, disease research, and personalized medicine. These specialists typically have strong skills in programming, statistics, biology, and data analysis, and collaborate with researchers and healthcare professionals through digital communication tools.

What is the difference between Remote Bioinformatics Machine Learning vs Remote Computational Biologist?

AspectRemote Bioinformatics Machine LearningRemote Computational Biologist
Required CredentialsMaster's or PhD in Bioinformatics, Computer Science, or related fields; experience in machine learningMaster's or PhD in Biology, Bioinformatics, or related fields; strong computational skills
Work EnvironmentRemote, collaborative teams in biotech, pharma, or research institutionsRemote or on-site, working in research labs or academic settings
Industry UsageUsed in biotech, healthcare, and pharmaceutical industries for data analysis and model developmentCommon in academic research, biotech, and healthcare for biological data interpretation

Remote Bioinformatics Machine Learning focuses on developing algorithms and models to analyze biological data using machine learning techniques. In contrast, Remote Computational Biologist applies computational methods to biological research questions, often integrating diverse data types. Both roles require strong computational skills and often overlap, but the former emphasizes machine learning expertise, while the latter has a broader biological research scope.

What are the most commonly searched types of Bioinformatics Machine Learning jobs in Connecticut? The most popular types of Bioinformatics Machine Learning jobs in Connecticut are:
What are popular job titles related to Remote Bioinformatics Machine Learning jobs in Connecticut? For Remote Bioinformatics Machine Learning jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Remote Bioinformatics Machine Learning jobs in Connecticut look for? The top searched job categories for Remote Bioinformatics Machine Learning jobs in Connecticut are:
What cities in Connecticut are hiring for Remote Bioinformatics Machine Learning jobs? Cities in Connecticut with the most Remote Bioinformatics Machine Learning job openings:
Remote Bioinformatics Machine Learning jobs near you
Scientist- Bioinformatics R&D -REMOTE

Scientist- Bioinformatics R&D -REMOTE

SEMA4

Stamford, CT • On-site, Remote

Full-time

Posted 22 days ago

Job description

Sema4 is a patient-centered health intelligence company dedicated to advancing healthcare through data-driven insights. Sema4 is transforming healthcare by applying AI and machine learning to multidimensional, longitudinal clinical and genomic data to build dynamic models of human health and defining optimal, individualized health trajectories. Centrellis®, our innovative health intelligence platform, is enabling us to generate a more complete understanding of disease and wellness and to provide science-driven solutions to the most pressing medical needs. Sema4 believes that patients should be treated as partners, and that data should be shared for the benefit of all.
We are looking for a talented Scientist- Bioinformatics R&D tojoin our team. The Bioinformatics Scientist leads translational bioinformatics and product development for NGS pipelines as part of the R&D Bioinformatics department. This scientist is an integral part of an interdisciplinary team that develops computational methods and pipelines to interpret large-scale human genome and transcriptome sequencing data from reproductive health, cancer, and other diseases. As part of a development team of engineers and scientists, this scientist will translate research prototypes into production-quality, scalable pipeline products used by a variety of clinical diagnostics and research projects across many teams at Sema4. This scientist will serve as an authority in these products to other users and teams and optimize them to serve Sema4 data science needs.
RESPONSIBILITIES
  • Design, develop, and test NGS pipelines for clinical tests and research projects in oncology, reproductive health, and other indications.
  • Lead or support bioinformatics projects to translate NGS results, as well as public and internal genomic, phenotype, and clinical/EMR datasets, to features and optimizations of clinical utility.
  • Analyze and integrate heterogeneous NGS data (somatic and germline SNVs, indel variants, copy-number alterations, structural variants, gene fusions, transcript isoforms, RNA abundance, RNA editing and modification) from diverse next-generation sequencing assays (Illumina, Ion Torrent, Pacific Biosciences; targeted panels, whole-exome sequencing, whole-genome sequencing, RNA-Seq; bulk and single-cell) and microarrays.
  • Work with wet labs and clinical teams to plan and design experiments to generate such data, and analyze this data.
  • Communicate effectively with collaborators (computational and bioinformatics scientists on R&D and production teams, IT/HPC, clinical lab directors, knowledgebase and curation teams, wet lab staff) to understand and satisfy product and research analysis needs.

QUALIFICATIONS
  • PhD in Bioinformatics, Biomedical Informatics, Computational Biology, Genomics, or a related discipline requiring strong computational and analytical skills supplemented with biology background
  • Hands-on experience working with NGS tools with high proficiency, especially for sequence analysis and expression analysis
  • Strong coding proficiency in R, Python, and SQL programming languages in a Linux environment.
  • Well-versed in the art of effective communication on interdisciplinary teams (scientists, programmers, and clinicians), especially graphical communication about high-complexity datasets to scientific audiences from different backgrounds.
  • High self-motivation, great ability to work in both multiple-task and independent fashions.
  • Good understanding of molecular, cell, and developmental biology, especially where relevant to cancer genomics, oncology, or endocrine neoplasms, and especially molecular cloning and NGS library preparation methodologies.
  • Developing code using distributed version control tools (especially Git) and software issue tracking/management systems (especially Jira).
  • Using or developing genome browsers or other tools for visualization of genomic datasets.

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