ZipRecruiter
Computational Biologist - Scientific Operations
ZipRecruiter, Boston, Massachusetts, us, 02298
Overview
Job Title:
Computational Biologist – Scientific Operations/Customer Success Location:
Boston, MA Type:
Full-Time Department:
Computational Biology & Data Science Job Description
We are seeking a highly skilled and customer-focused
Computational Biologist
to join our team and play a pivotal role in the success of our scientific collaboration and service project customers. This position blends cutting-edge computational biology with direct client engagement — from executing spatial transcriptomics service projects to troubleshooting pipelines and delivering clear, impactful results. The ideal candidate will have a strong background in image analysis, machine learning, and data pipeline optimization, combined with exceptional communication skills to guide customers through complex datasets. You will work closely with our internal teams to ensure every project delivers actionable insights, and you will be a key driver of customer success with our STARmap assay on the Pyxa instrument. We welcome candidates with
Bachelor’s, Master’s, or Ph.D. degrees
in computational or quantitative biology-related fields. Responsibilities
Lead the computational execution of image analysis and spatial transcriptomics service projects for pharmaceutical, biotech, and academic collaborators. Prepare detailed, high-quality reports for customers, explaining results, methods, and interpretations in clear and actionable form. Serve as the primary point of contact for assigned projects, managing timelines and ensuring client expectations are exceeded. Develop and optimize analysis pipelines for new tissue types for the STARmap assay on the Pyxa instrument. Build rapid-debugging tools to identify and resolve pipeline performance issues quickly. Collaborate with assay development and software teams to implement pipeline improvements based on customer feedback and performance metrics. Apply advanced image analysis, statistical modeling, and machine learning approaches to spatial transcriptomics datasets. Stay current with developments in image analysis, spatial biology, and AI/ML to integrate state-of-the-art methods into production pipelines. Contribute to knowledge sharing across the team through documentation, reusable code, and training materials. Qualifications
Bachelor’s, Master’s, or Ph.D. in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, or related field. 2–5 years (industry or postdoctoral) experience in image analysis, spatial transcriptomics/genomics, and machine learning. Strong programming skills in Python and R with expertise in packages such as scikit-image, scikit-learn, TensorFlow, or PyTorch. Proven track record in developing, maintaining, and troubleshooting computational analysis pipelines. Experience communicating technical findings to both technical and non-technical stakeholders, particularly in a customer-facing context. Familiarity with high-performance and/or cloud computing environments. Passion for enabling scientific discovery through a combination of computational rigor and outstanding customer engagement. GitHub profile, code samples, or portfolio showcasing relevant work is highly encouraged.
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Job Title:
Computational Biologist – Scientific Operations/Customer Success Location:
Boston, MA Type:
Full-Time Department:
Computational Biology & Data Science Job Description
We are seeking a highly skilled and customer-focused
Computational Biologist
to join our team and play a pivotal role in the success of our scientific collaboration and service project customers. This position blends cutting-edge computational biology with direct client engagement — from executing spatial transcriptomics service projects to troubleshooting pipelines and delivering clear, impactful results. The ideal candidate will have a strong background in image analysis, machine learning, and data pipeline optimization, combined with exceptional communication skills to guide customers through complex datasets. You will work closely with our internal teams to ensure every project delivers actionable insights, and you will be a key driver of customer success with our STARmap assay on the Pyxa instrument. We welcome candidates with
Bachelor’s, Master’s, or Ph.D. degrees
in computational or quantitative biology-related fields. Responsibilities
Lead the computational execution of image analysis and spatial transcriptomics service projects for pharmaceutical, biotech, and academic collaborators. Prepare detailed, high-quality reports for customers, explaining results, methods, and interpretations in clear and actionable form. Serve as the primary point of contact for assigned projects, managing timelines and ensuring client expectations are exceeded. Develop and optimize analysis pipelines for new tissue types for the STARmap assay on the Pyxa instrument. Build rapid-debugging tools to identify and resolve pipeline performance issues quickly. Collaborate with assay development and software teams to implement pipeline improvements based on customer feedback and performance metrics. Apply advanced image analysis, statistical modeling, and machine learning approaches to spatial transcriptomics datasets. Stay current with developments in image analysis, spatial biology, and AI/ML to integrate state-of-the-art methods into production pipelines. Contribute to knowledge sharing across the team through documentation, reusable code, and training materials. Qualifications
Bachelor’s, Master’s, or Ph.D. in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, or related field. 2–5 years (industry or postdoctoral) experience in image analysis, spatial transcriptomics/genomics, and machine learning. Strong programming skills in Python and R with expertise in packages such as scikit-image, scikit-learn, TensorFlow, or PyTorch. Proven track record in developing, maintaining, and troubleshooting computational analysis pipelines. Experience communicating technical findings to both technical and non-technical stakeholders, particularly in a customer-facing context. Familiarity with high-performance and/or cloud computing environments. Passion for enabling scientific discovery through a combination of computational rigor and outstanding customer engagement. GitHub profile, code samples, or portfolio showcasing relevant work is highly encouraged.
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